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Explanation of training model #16

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kennis222 opened this issue Mar 22, 2022 · 1 comment
Open

Explanation of training model #16

kennis222 opened this issue Mar 22, 2022 · 1 comment

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@kennis222
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HI developer,

Thanks for developing this package.

May I ask how to read the training model details? For example, I use XGboost model as the estimator and put into the NARX model. After that I would like to use SHAP get the related explanations. Can I do like this?

BTW, I would like to confirm the meaning of exog_order, for example, exog_order = np.tile(10,6)). Does the 10 represent the the lag order of the exogenous variable and the 6 represent that "there are 6 exogenous variables", although there are 6 exogenous variables in the X_train?

@kennis222
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Hi contributors,

I think to utilize the model.base_estimator that can get the model parameters such as using the xgboost model. Because the narx model can automatically help proceed the lag feature, such as either the target y in lags or the independent variables X in lags. However, I would like to get the actual feature name instead of feature names as f0, f1, f2 etc., as must be obvious.

Then, I try to use model.get_booster().feature_names to capture the feature names. However, nothing displays.

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