Проблема, возникшая при использовании GridsearchCV в randomForestClassifier ⇐ Python
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Anonymous
Проблема, возникшая при использовании GridsearchCV в randomForestClassifier
I am a beginner and I am working on a classification problem related to heart disease using RandomForestClassifier. While performing hyperparameter tuning on RandomForestClassifier, I am facing the following issue. I am using sklearn Pipeline and ColumnTransformer for preprocessing.
Error: 720 fits failed out of a total of 2160. The score on these train-test partitions for these parameters will be set to nan. If these failures are not expected, you can try to debug them by setting error_score='raise'. UserWarning: One or more of the test scores are non-finite numerical_pipeline = Pipeline( steps=[('scaler',StandardScaler())] ) categorical_pipeline = Pipeline( steps=[('encoder',OneHotEncoder(handle_unknown='ignore'))] ) preprocessor = ColumnTransformer( [('numerical_pipeline',numerical_pipeline,numerical_features), ('categorical_pipeline',categorical_pipeline,categorical_features)]` X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)` scaled_X_train = preprocessor.fit_transform(X_train) scaled_X_test = preprocessor.transform(X_test)` param_grid={'max_depth':[3,5,10,None], 'n_estimators':[10,100,200], 'max_features':[1,3,5,7], 'min_samples_leaf':[1,2,3], 'min_samples_split':[1,2,3] } grid = GridSearchCV(RandomForestClassifier(),param_grid=param_grid,cv=5,scoring='accuracy',verbose=True,n_jobs=-1) grid.fit(scaled_X_train,y_train)
Источник: https://stackoverflow.com/questions/781 ... classifier
I am a beginner and I am working on a classification problem related to heart disease using RandomForestClassifier. While performing hyperparameter tuning on RandomForestClassifier, I am facing the following issue. I am using sklearn Pipeline and ColumnTransformer for preprocessing.
Error: 720 fits failed out of a total of 2160. The score on these train-test partitions for these parameters will be set to nan. If these failures are not expected, you can try to debug them by setting error_score='raise'. UserWarning: One or more of the test scores are non-finite numerical_pipeline = Pipeline( steps=[('scaler',StandardScaler())] ) categorical_pipeline = Pipeline( steps=[('encoder',OneHotEncoder(handle_unknown='ignore'))] ) preprocessor = ColumnTransformer( [('numerical_pipeline',numerical_pipeline,numerical_features), ('categorical_pipeline',categorical_pipeline,categorical_features)]` X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)` scaled_X_train = preprocessor.fit_transform(X_train) scaled_X_test = preprocessor.transform(X_test)` param_grid={'max_depth':[3,5,10,None], 'n_estimators':[10,100,200], 'max_features':[1,3,5,7], 'min_samples_leaf':[1,2,3], 'min_samples_split':[1,2,3] } grid = GridSearchCV(RandomForestClassifier(),param_grid=param_grid,cv=5,scoring='accuracy',verbose=True,n_jobs=-1) grid.fit(scaled_X_train,y_train)
Источник: https://stackoverflow.com/questions/781 ... classifier
