expfrom sklearn.linear_model import LogisticRegressiondef predict(row, coefficients): for i in range(len(row)-1): return 1.0 / (1.0 + exp(-yhat))
def co
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# These are the (standardized) coefficients foundlist(zip(X.columns= pd.DataFrame(list(zip(X_train.columns, LGBMR.feature_importances_)))
df_LGBM_model_coefficients.rename(columns={0:'Features', 1: 'Coefficients'},