notebooks/42 - Machine learning interpretability.ipynb

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   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "# Machine learning interpretability\n",
    "\n",
    "In modern day machine learning it is important to be able to explain how our models \"think\". A simple accuracy score isn't enough. This notebook explores the lesson on interpretability.\n",
    "\n",
    "Machine learning interpretability is an increasingly important topic in artificial intelligence. \n",
    "\n",
    "As machine learning models become more complex, understanding how they make predictions is becoming more difficult. This lack of transparency can lead to a lack of trust in the model. It can make it difficult to identify and correct errors. Interpretability is the ability to explain how a machine learning model arrived at a particular decision. It is essential to build trust and understanding in these powerful tools. \n",
    "\n",
    "This notebook will explore the importance of interpretability and provide practical examples of how it can be achieved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How To"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"data/housing.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_, y_train, y_ = train_test_split(df.drop([\"longitude\",\"latitude\", \"ocean_proximity\", \"median_house_value\"], axis=1), \n",
    "                                                    df.median_house_value, test_size=.5, stratify=df.ocean_proximity)\n",
    "\n",
    "x_val, x_test, y_val, y_test = train_test_split(x_, y_, test_size=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6556994491877645"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Influence of Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\tools\\Anaconda3\\envs\\skillshare\\lib\\site-packages\\sklearn\\utils\\deprecation.py:143: FutureWarning: The sklearn.metrics.scorer module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "C:\\tools\\Anaconda3\\envs\\skillshare\\lib\\site-packages\\sklearn\\utils\\deprecation.py:143: FutureWarning: The sklearn.feature_selection.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.feature_selection. Anything that cannot be imported from sklearn.feature_selection is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import eli5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "                x4\n",
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      ],
      "text/plain": [
       "Explanation(estimator='RandomForestRegressor()', description='\\nRandom forest feature importances; values are numbers 0 <= x <= 1;\\nall values sum to 1.\\n', error=None, method='feature importances', is_regression=True, targets=None, feature_importances=FeatureImportances(importances=[FeatureWeight(feature='x5', weight=0.5667877033603446, std=0.00841680749109398, value=None), FeatureWeight(feature='x3', weight=0.10564312361305908, std=0.006052261117665171, value=None), FeatureWeight(feature='x0', weight=0.09566374264038832, std=0.005333010779630694, value=None), FeatureWeight(feature='x2', weight=0.09023838276674524, std=0.006687473112443481, value=None), FeatureWeight(feature='x1', weight=0.07361192693024816, std=0.006256139007962976, value=None), FeatureWeight(feature='x4', weight=0.06805512068921464, std=0.006545124891306123, value=None)], remaining=0), decision_tree=None, highlight_spaces=None, transition_features=None, image=None)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eli5.explain_weights(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "        <p style=\"margin-bottom: 0.5em; margin-top: 0em\">\n",
       "            <b>\n",
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       "        y\n",
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       "\n",
       "    \n",
       "    (score <b>268513.000</b>)\n",
       "\n",
       "top features\n",
       "        </p>\n",
       "    \n",
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       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;\">\n",
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       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if &quot;show_feature_values&quot; is True.\">\n",
       "                    Contribution<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
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       "        +206727.112\n",
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       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        &lt;BIAS&gt;\n",
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       "            <tr style=\"background-color: hsl(120, 100.00%, 90.82%); border: none;\">\n",
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       "        +67946.952\n",
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       "        median_income\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 96.49%); border: none;\">\n",
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       "        +17204.413\n",
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       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        total_bedrooms\n",
       "    </td>\n",
       "    \n",
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       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 98.67%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +4310.367\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        housing_median_age\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 99.61%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -753.897\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        total_rooms\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 99.54%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -945.697\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        households\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 95.32%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -25976.250\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        population\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
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       "Explanation(estimator='RandomForestRegressor()', description='\\nFeatures with largest coefficients.\\n\\nFeature weights are calculated by following decision paths in trees\\nof an ensemble (or a single tree for DecisionTreeClassifier).\\nEach node of the tree has an output score, and contribution of a feature\\non the decision path is how much the score changes from parent to child.\\nWeights of all features sum to the output score or proba of the estimator.\\n\\nCaveats:\\n1. Feature weights just show if the feature contributed positively or\\n   negatively to the final score, and does not show how increasing or\\n   decreasing the feature value will change the prediction.\\n2. In some cases, feature weight can be close to zero for an important feature.\\n   For example, in a single tree that computes XOR function, the feature at the\\n   top of the tree will have zero weight because expected scores for both\\n   branches are equal, so decision at the top feature does not change the\\n   expected score. For an ensemble predicting XOR functions it might not be\\n   a problem, but it is not reliable if most trees happen to choose the same\\n   feature at the top.\\n', error=None, method='decision path', is_regression=True, targets=[TargetExplanation(target='y', feature_weights=FeatureWeights(pos=[FeatureWeight(feature='<BIAS>', weight=206727.1120379797, std=None, value=1.0), FeatureWeight(feature='median_income', weight=67946.95215258111, std=None, value=5.4562), FeatureWeight(feature='total_bedrooms', weight=17204.41294790117, std=None, value=667.0), FeatureWeight(feature='housing_median_age', weight=4310.366776858451, std=None, value=28.0)], neg=[FeatureWeight(feature='population', weight=-25976.249508324312, std=None, value=2048.0), FeatureWeight(feature='households', weight=-945.6970320239207, std=None, value=685.0), FeatureWeight(feature='total_rooms', weight=-753.8973749721359, std=None, value=4485.0)], pos_remaining=0, neg_remaining=0), proba=None, score=268513.0, weighted_spans=None, heatmap=None)], feature_importances=None, decision_tree=None, highlight_spaces=None, transition_features=None, image=None)"
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       "    \n",
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       "\n",
       "    \n",
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       "\n",
       "        \n",
       "            \n",
       "                \n",
       "                \n",
       "    \n",
       "        <p style=\"margin-bottom: 0.5em; margin-top: 0em\">\n",
       "            <b>\n",
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       "        y\n",
       "    \n",
       "</b>\n",
       "\n",
       "    \n",
       "    (score <b>147431.000</b>)\n",
       "\n",
       "top features\n",
       "        </p>\n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if &quot;show_feature_values&quot; is True.\">\n",
       "                    Contribution<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +206727.112\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        &lt;BIAS&gt;\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 95.72%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +22868.678\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        total_rooms\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 98.94%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -3110.917\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        households\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 98.17%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -6803.699\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        total_bedrooms\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 97.74%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -9159.044\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        housing_median_age\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 96.04%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -20468.350\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        population\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 93.38%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -42622.781\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        median_income\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "            \n",
       "        \n",
       "\n",
       "        \n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Explanation(estimator='RandomForestRegressor()', description='\\nFeatures with largest coefficients.\\n\\nFeature weights are calculated by following decision paths in trees\\nof an ensemble (or a single tree for DecisionTreeClassifier).\\nEach node of the tree has an output score, and contribution of a feature\\non the decision path is how much the score changes from parent to child.\\nWeights of all features sum to the output score or proba of the estimator.\\n\\nCaveats:\\n1. Feature weights just show if the feature contributed positively or\\n   negatively to the final score, and does not show how increasing or\\n   decreasing the feature value will change the prediction.\\n2. In some cases, feature weight can be close to zero for an important feature.\\n   For example, in a single tree that computes XOR function, the feature at the\\n   top of the tree will have zero weight because expected scores for both\\n   branches are equal, so decision at the top feature does not change the\\n   expected score. For an ensemble predicting XOR functions it might not be\\n   a problem, but it is not reliable if most trees happen to choose the same\\n   feature at the top.\\n', error=None, method='decision path', is_regression=True, targets=[TargetExplanation(target='y', feature_weights=FeatureWeights(pos=[FeatureWeight(feature='<BIAS>', weight=206727.1120379797, std=None, value=1.0), FeatureWeight(feature='total_rooms', weight=22868.678498102803, std=None, value=1228.0)], neg=[FeatureWeight(feature='median_income', weight=-42622.78104499923, std=None, value=3.0225), FeatureWeight(feature='population', weight=-20468.349589479698, std=None, value=1603.0), FeatureWeight(feature='housing_median_age', weight=-9159.04427985055, std=None, value=30.0), FeatureWeight(feature='total_bedrooms', weight=-6803.698911011207, std=None, value=358.0), FeatureWeight(feature='households', weight=-3110.9167107417757, std=None, value=323.0)], pos_remaining=0, neg_remaining=0), proba=None, score=147431.0, weighted_spans=None, heatmap=None)], feature_importances=None, decision_tree=None, highlight_spaces=None, transition_features=None, image=None)"
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       "\n",
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       "\n",
       "    \n",
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       "\n",
       "    \n",
       "\n",
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       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "    \n",
       "\n",
       "        \n",
       "            \n",
       "                \n",
       "                \n",
       "    \n",
       "        <p style=\"margin-bottom: 0.5em; margin-top: 0em\">\n",
       "            <b>\n",
       "    \n",
       "        y\n",
       "    \n",
       "</b>\n",
       "\n",
       "    \n",
       "    (score <b>216857.000</b>)\n",
       "\n",
       "top features\n",
       "        </p>\n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if &quot;show_feature_values&quot; is True.\">\n",
       "                    Contribution<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +206727.112\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        &lt;BIAS&gt;\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 96.49%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +17186.951\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        households\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 96.52%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +16984.921\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        population\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 96.79%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +15171.230\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        total_rooms\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 98.20%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +6615.827\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        housing_median_age\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 98.49%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +5175.545\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        total_bedrooms\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 92.49%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -51004.586\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        median_income\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "            \n",
       "        \n",
       "\n",
       "        \n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Explanation(estimator='RandomForestRegressor()', description='\\nFeatures with largest coefficients.\\n\\nFeature weights are calculated by following decision paths in trees\\nof an ensemble (or a single tree for DecisionTreeClassifier).\\nEach node of the tree has an output score, and contribution of a feature\\non the decision path is how much the score changes from parent to child.\\nWeights of all features sum to the output score or proba of the estimator.\\n\\nCaveats:\\n1. Feature weights just show if the feature contributed positively or\\n   negatively to the final score, and does not show how increasing or\\n   decreasing the feature value will change the prediction.\\n2. In some cases, feature weight can be close to zero for an important feature.\\n   For example, in a single tree that computes XOR function, the feature at the\\n   top of the tree will have zero weight because expected scores for both\\n   branches are equal, so decision at the top feature does not change the\\n   expected score. For an ensemble predicting XOR functions it might not be\\n   a problem, but it is not reliable if most trees happen to choose the same\\n   feature at the top.\\n', error=None, method='decision path', is_regression=True, targets=[TargetExplanation(target='y', feature_weights=FeatureWeights(pos=[FeatureWeight(feature='<BIAS>', weight=206727.1120379797, std=None, value=1.0), FeatureWeight(feature='households', weight=17186.951141769227, std=None, value=308.0), FeatureWeight(feature='population', weight=16984.921268894872, std=None, value=687.0), FeatureWeight(feature='total_rooms', weight=15171.229967841553, std=None, value=1125.0), FeatureWeight(feature='housing_median_age', weight=6615.827231125529, std=None, value=23.0), FeatureWeight(feature='total_bedrooms', weight=5175.544844203078, std=None, value=273.0)], neg=[FeatureWeight(feature='median_income', weight=-51004.58649181392, std=None, value=2.3182)], pos_remaining=0, neg_remaining=0), proba=None, score=216857.0, weighted_spans=None, heatmap=None)], feature_importances=None, decision_tree=None, highlight_spaces=None, transition_features=None, image=None)"
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       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       "        \n",
       "            \n",
       "                \n",
       "                \n",
       "    \n",
       "        <p style=\"margin-bottom: 0.5em; margin-top: 0em\">\n",
       "            <b>\n",
       "    \n",
       "        y\n",
       "    \n",
       "</b>\n",
       "\n",
       "    \n",
       "    (score <b>217626.000</b>)\n",
       "\n",
       "top features\n",
       "        </p>\n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if &quot;show_feature_values&quot; is True.\">\n",
       "                    Contribution<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +206727.112\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        &lt;BIAS&gt;\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 91.77%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +58160.159\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        total_rooms\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 94.57%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +32081.634\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        households\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 97.09%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +13152.518\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        total_bedrooms\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 97.90%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +8281.158\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        housing_median_age\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 98.01%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -7645.868\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        population\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 88.55%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -93130.714\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        median_income\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "            \n",
       "        \n",
       "\n",
       "        \n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Explanation(estimator='RandomForestRegressor()', description='\\nFeatures with largest coefficients.\\n\\nFeature weights are calculated by following decision paths in trees\\nof an ensemble (or a single tree for DecisionTreeClassifier).\\nEach node of the tree has an output score, and contribution of a feature\\non the decision path is how much the score changes from parent to child.\\nWeights of all features sum to the output score or proba of the estimator.\\n\\nCaveats:\\n1. Feature weights just show if the feature contributed positively or\\n   negatively to the final score, and does not show how increasing or\\n   decreasing the feature value will change the prediction.\\n2. In some cases, feature weight can be close to zero for an important feature.\\n   For example, in a single tree that computes XOR function, the feature at the\\n   top of the tree will have zero weight because expected scores for both\\n   branches are equal, so decision at the top feature does not change the\\n   expected score. For an ensemble predicting XOR functions it might not be\\n   a problem, but it is not reliable if most trees happen to choose the same\\n   feature at the top.\\n', error=None, method='decision path', is_regression=True, targets=[TargetExplanation(target='y', feature_weights=FeatureWeights(pos=[FeatureWeight(feature='<BIAS>', weight=206727.1120379797, std=None, value=1.0), FeatureWeight(feature='total_rooms', weight=58160.15902547827, std=None, value=1951.0), FeatureWeight(feature='households', weight=32081.634140011814, std=None, value=813.0), FeatureWeight(feature='total_bedrooms', weight=13152.51803503369, std=None, value=846.0), FeatureWeight(feature='housing_median_age', weight=8281.158449922435, std=None, value=42.0)], neg=[FeatureWeight(feature='median_income', weight=-93130.71359036738, std=None, value=1.5195), FeatureWeight(feature='population', weight=-7645.86809805855, std=None, value=2500.0)], pos_remaining=0, neg_remaining=0), proba=None, score=217626.0, weighted_spans=None, heatmap=None)], feature_importances=None, decision_tree=None, highlight_spaces=None, transition_features=None, image=None)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
       "        filter: brightness(85%);\n",
       "    }\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
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       "\n",
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       "        \n",
       "\n",
       "    \n",
       "\n",
       "        \n",
       "            \n",
       "                \n",
       "                \n",
       "    \n",
       "        <p style=\"margin-bottom: 0.5em; margin-top: 0em\">\n",
       "            <b>\n",
       "    \n",
       "        y\n",
       "    \n",
       "</b>\n",
       "\n",
       "    \n",
       "    (score <b>370269.130</b>)\n",
       "\n",
       "top features\n",
       "        </p>\n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if &quot;show_feature_values&quot; is True.\">\n",
       "                    Contribution<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +206727.112\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        &lt;BIAS&gt;\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.69%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +91543.225\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        population\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 93.17%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +44576.228\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        total_bedrooms\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 96.77%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +15257.757\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        households\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 96.78%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +15210.430\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        total_rooms\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 96.99%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +13843.872\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        housing_median_age\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 96.54%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -16889.494\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        median_income\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "            \n",
       "        \n",
       "\n",
       "        \n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
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       "\n",
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       "\n",
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       "\n",
       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       "\n",
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       "\n",
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       "\n",
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       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Explanation(estimator='RandomForestRegressor()', description='\\nFeatures with largest coefficients.\\n\\nFeature weights are calculated by following decision paths in trees\\nof an ensemble (or a single tree for DecisionTreeClassifier).\\nEach node of the tree has an output score, and contribution of a feature\\non the decision path is how much the score changes from parent to child.\\nWeights of all features sum to the output score or proba of the estimator.\\n\\nCaveats:\\n1. Feature weights just show if the feature contributed positively or\\n   negatively to the final score, and does not show how increasing or\\n   decreasing the feature value will change the prediction.\\n2. In some cases, feature weight can be close to zero for an important feature.\\n   For example, in a single tree that computes XOR function, the feature at the\\n   top of the tree will have zero weight because expected scores for both\\n   branches are equal, so decision at the top feature does not change the\\n   expected score. For an ensemble predicting XOR functions it might not be\\n   a problem, but it is not reliable if most trees happen to choose the same\\n   feature at the top.\\n', error=None, method='decision path', is_regression=True, targets=[TargetExplanation(target='y', feature_weights=FeatureWeights(pos=[FeatureWeight(feature='<BIAS>', weight=206727.1120379797, std=None, value=1.0), FeatureWeight(feature='population', weight=91543.22530016846, std=None, value=1026.0), FeatureWeight(feature='total_bedrooms', weight=44576.227773894316, std=None, value=687.0), FeatureWeight(feature='households', weight=15257.75688900795, std=None, value=592.0), FeatureWeight(feature='total_rooms', weight=15210.429938229026, std=None, value=2075.0), FeatureWeight(feature='housing_median_age', weight=13843.871962384443, std=None, value=30.0)], neg=[FeatureWeight(feature='median_income', weight=-16889.49390166382, std=None, value=3.1635)], pos_remaining=0, neg_remaining=0), proba=None, score=370269.13, weighted_spans=None, heatmap=None)], feature_importances=None, decision_tree=None, highlight_spaces=None, transition_features=None, image=None)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for x in range(5):\n",
    "    display(eli5.explain_prediction(model, x_train.iloc[x, :]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.inspection import permutation_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'importances_mean': array([0.30626287, 0.20438274, 0.49193051, 0.39923199, 0.23444423,\n",
       "        1.56607312]),\n",
       " 'importances_std': array([0.00300465, 0.00150659, 0.00796738, 0.00607609, 0.00408509,\n",
       "        0.01063025]),\n",
       " 'importances': array([[0.30934077, 0.30191579, 0.30701877, 0.30368526, 0.30935374],\n",
       "        [0.20672229, 0.20405023, 0.20400822, 0.20210153, 0.20503143],\n",
       "        [0.47992992, 0.49622957, 0.49475018, 0.50258828, 0.48615459],\n",
       "        [0.40668523, 0.38972197, 0.39519039, 0.40363647, 0.40092587],\n",
       "        [0.23050688, 0.2363061 , 0.24161848, 0.23186354, 0.23192618],\n",
       "        [1.56854817, 1.5623001 , 1.54913014, 1.58194708, 1.56844011]])}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permutation_importance(model, x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.inspection import plot_partial_dependence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.inspection._plot.partial_dependence.PartialDependenceDisplay at 0x25ecd150b80>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_partial_dependence(model, x_train, x_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n"
     ]
    }
   ],
   "source": [
    "expl = shap.TreeExplainer(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Passing 10216 background samples may lead to slow runtimes. Consider using shap.sample(data, 100) to create a smaller background data set.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<shap.explainers.tree.TreeExplainer at 0x25ecd818310>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.TreeExplainer(model, data=x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_val = expl.shap_values(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"model output value\"], \"baseValue\": 206727.11203797962, \"outValue\": 98528.99999999777, \"link\": \"identity\", \"featureNames\": [\"housing_median_age\", \"total_rooms\", \"total_bedrooms\", \"population\", \"households\", \"median_income\"], \"features\": {\"0\": {\"effect\": -1059.3643867635328, \"value\": 29.0}, \"1\": {\"effect\": 8619.314996114517, \"value\": 1202.0}, \"2\": {\"effect\": -7443.735928530613, \"value\": 290.0}, \"3\": {\"effect\": 34437.79532256012, \"value\": 383.0}, \"4\": {\"effect\": -24537.044661326177, \"value\": 156.0}, \"5\": {\"effect\": -118215.07738003616, \"value\": 1.3371}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iDXCGUVNBCKH08TWSP4Q9')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(expl.expected_value, shap_val[0, :], x_val.iloc[0, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Check out `shap` further and see which plots you can generate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [shap](https://github.com/slundberg/shap)\n",
    "- [Scikit Yellowbrick](https://www.scikit-yb.org/en/latest/)"
   ]
  }
 ],
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