model_performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "performance = {\n",
    "    'dummy': 0.5032\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bottom': 0, 'top': 1}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(\n",
    "    performance.keys(),\n",
    "    performance.values(),\n",
    "    color='black'\n",
    ")\n",
    "plt.ylim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}