![]() ![]() > 355 return self.func(*args, **kwargs)ģ57 def call_and_shelve(self, *args, **kwargs): opt/conda/lib/python3.6/site-packages/joblib/memory.py in call(self, *args, **kwargs) This is necessary when loading the transformer opt/conda/lib/python3.6/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)ģ14 # Replace the transformer of the step with the fittedģ15 # transformer. opt/conda/lib/python3.6/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params) > 2540 return(pipe.fit_transform(train_data)) opt/conda/lib/python3.6/site-packages/pycaret/preprocess.py in Preprocess_Path_One(train_data, target_variable, ml_usecase, test_data, categorical_features, numerical_features, time_features, features_todrop, display_types, imputation_type, numeric_imputation_strategy, categorical_imputation_strategy, apply_zero_nearZero_variance, club_rare_levels, rara_level_threshold_percentage, apply_untrained_levels_treatment, untrained_levels_treatment_method, apply_ordinal_encoding, ordinal_columns_and_categories, apply_cardinality_reduction, cardinal_method, cardinal_features, apply_binning, features_to_binn, apply_grouping, group_name, features_to_group_ListofList, apply_polynomial_trigonometry_features, max_polynomial, trigonometry_calculations, top_poly_trig_features_to_select_percentage, scale_data, scaling_method, Power_transform_data, Power_transform_method, target_transformation, target_transformation_method, remove_outliers, outlier_contamination_percentage, outlier_methods, apply_feature_selection, feature_selection_top_features_percentage, remove_multicollinearity, maximum_correlation_between_features, remove_perfect_collinearity, apply_feature_interactions, feature_interactions_to_apply, feature_interactions_top_features_to_select_percentage, cluster_entire_data, range_of_clusters_to_try, apply_pca, pca_method, pca_variance_retained_or_number_of_components, random_state)Ģ538 return(pipe.fit_transform(train_data),ansform(test_data)) opt/conda/lib/python3.6/site-packages/pycaret/regression.py in setup(data, target, train_size, sampling, sample_estimator, categorical_features, categorical_imputation, ordinal_features, high_cardinality_features, high_cardinality_method, numeric_features, numeric_imputation, date_features, ignore_features, normalize, normalize_method, transformation, transformation_method, handle_unknown_categorical, unknown_categorical_method, pca, pca_method, pca_components, ignore_low_variance, combine_rare_levels, rare_level_threshold, bin_numeric_features, remove_outliers, outliers_threshold, remove_multicollinearity, multicollinearity_threshold, create_clusters, cluster_iter, polynomial_features, polynomial_degree, trigonometry_features, polynomial_threshold, group_features, group_names, feature_selection, feature_selection_threshold, feature_interaction, feature_ratio, interaction_threshold, transform_target, transform_target_method, session_id, silent, profile)ĩ55 target_transformation = transform_target, #newĩ56 target_transformation_method = transform_target_method_pass, #new > 1 setup_data1 = setup(data = df, target = 'Salary', session_id=123) ![]() ValueError Traceback (most recent call last) ![]() ![]() Setup_data1 = setup(data = df, target = 'Salary', session_id=123) Df=pd.read_csv("/kaggle/input/salary-data-simple-linear-regression/Salary_Data.csv")` ![]()
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