generalization #11
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@ -63,6 +63,7 @@ model_result = pd.DataFrame(columns= ["Model", "Accuracy", "Recall", "F1_score",
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# Naive Bayes
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model_result = pipeline_naiveBayes_benchmark(X_train, y_train, X_test, y_test, model_result)
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save_result_set_s3(model_result , "resultat", type_of_activity, type_of_model)
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print("Naive Bayes : Done")
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# Logistic Regression
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@ -70,12 +71,16 @@ model_result = pipeline_logreg_benchmark(X_train, y_train, X_test, y_test, model
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print("Logistic : Done")
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model_result = pipeline_logreg_cv(X_train, y_train, X_test, y_test, model_result)
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save_result_set_s3(model_result , "resultat", type_of_activity, type_of_model)
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print("Logistic CV : Done")
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# Random Forest
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model_result = pipeline_randomF_benchmark(X_train, y_train, X_test, y_test, model_result)
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save_result_set_s3(model_result , "resultat", type_of_activity, type_of_model)
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print("Random Forest : Done")
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model_result = pipeline_randomF_cv(X_train, y_train, X_test, y_test, model_result)
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save_result_set_s3(model_result , "resultat", type_of_activity, type_of_model)
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print("Random Forest CV: Done")
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# Save result
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Reference in New Issue
Block a user