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10587706

Run 10587706

Task 211690 (Supervised Regression) liver-disorders Uploaded 25-02-2022 by jordan porter
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pip eline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sk learn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sk learn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.f eature_selection._univariate_selection.SelectPercentile,model=sklearn.linea r_model._glm.glm.PoissonRegressor))(1)Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and "inverse_transform" if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor))(1)_cvnull
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor))(1)_error_scoreNaN
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor))(1)_n_jobsnull
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor))(1)_param_grid{"feature_select__percentile": [20, 40, 60, 80], "model__alpha": [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09], "model__max_iter": [200, 500, 800], "poly__degree": [1, 2]}
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor))(1)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor))(1)_refittrue
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor))(1)_return_train_scorefalse
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor))(1)_scoring"neg_root_mean_squared_error"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor))(1)_verbose0
sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor)(1)_memorynull
sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "ct", "step_name": "ct"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "poly", "step_name": "poly"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "feature_select", "step_name": "feature_select"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "model", "step_name": "model"}}]
sklearn.pipeline.Pipeline(ct=sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans),poly=sklearn.preprocessing._polynomial.PolynomialFeatures,feature_select=sklearn.feature_selection._univariate_selection.SelectPercentile,model=sklearn.linear_model._glm.glm.PoissonRegressor)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "log10", "step_name": "log10", "argument_1": ["gammagt", "sgot", "sgpt"]}}]
sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(remainder=sklearn.preprocessing._data.StandardScaler,log10=Log_Trans.Log_Trans)(1)_verbose_feature_names_outtrue
sklearn.preprocessing._data.StandardScaler(11)_copytrue
sklearn.preprocessing._data.StandardScaler(11)_with_meantrue
sklearn.preprocessing._data.StandardScaler(11)_with_stdtrue
sklearn.preprocessing._polynomial.PolynomialFeatures(1)_degree2
sklearn.preprocessing._polynomial.PolynomialFeatures(1)_include_biastrue
sklearn.preprocessing._polynomial.PolynomialFeatures(1)_interaction_onlyfalse
sklearn.preprocessing._polynomial.PolynomialFeatures(1)_order"C"
sklearn.feature_selection._univariate_selection.SelectPercentile(2)_percentile70
sklearn.feature_selection._univariate_selection.SelectPercentile(2)_score_func{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.f_classif"}
sklearn.linear_model._glm.glm.PoissonRegressor(1)_alpha1.0
sklearn.linear_model._glm.glm.PoissonRegressor(1)_fit_intercepttrue
sklearn.linear_model._glm.glm.PoissonRegressor(1)_max_iter100
sklearn.linear_model._glm.glm.PoissonRegressor(1)_tol0.0001
sklearn.linear_model._glm.glm.PoissonRegressor(1)_verbose0
sklearn.linear_model._glm.glm.PoissonRegressor(1)_warm_startfalse

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

7 Evaluation measures

Mean prior absolute error
2.7953
Cross-validation details (33% Holdout set)
Number of instances
113
Cross-validation details (33% Holdout set)
Root mean prior squared error
3.5579
Cross-validation details (33% Holdout set)