Run
10154879

Run 10154879

Task 219 (Supervised Classification) electricity Uploaded 06-03-2019 by Pieter Gijsbers
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Flow

sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer .ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=c ategory_encoders.one_hot.OneHotEncoder),preprocessing2=sklearn.compose._col umn_transformer.ColumnTransformer(non_binary_imputer=__main__.SklearnSimple ImputerCloneWithMedianStrategy),algorithm=sklearn.ensemble.forest.RandomFor estClassifier)(1)Automatically created scikit-learn flow.
sklearn.impute.SimpleImputer(10)_copytrue
sklearn.impute.SimpleImputer(10)_fill_valuenull
sklearn.impute.SimpleImputer(10)_missing_valuesNaN
sklearn.impute.SimpleImputer(10)_strategy"mean"
sklearn.impute.SimpleImputer(10)_verbose0
category_encoders.one_hot.OneHotEncoder(1)_cols[0]
category_encoders.one_hot.OneHotEncoder(1)_drop_invariantfalse
category_encoders.one_hot.OneHotEncoder(1)_handle_unknown"impute"
category_encoders.one_hot.OneHotEncoder(1)_impute_missingtrue
category_encoders.one_hot.OneHotEncoder(1)_return_dftrue
category_encoders.one_hot.OneHotEncoder(1)_use_cat_namesfalse
category_encoders.one_hot.OneHotEncoder(1)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(51)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(51)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(51)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(51)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(51)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(51)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(51)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(51)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(51)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(51)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(51)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(51)_n_estimators"warn"
sklearn.ensemble.forest.RandomForestClassifier(51)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(51)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(51)_random_state42599
sklearn.ensemble.forest.RandomForestClassifier(51)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(51)_warm_startfalse
sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder)(1)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "num_imputer", "step_name": "num_imputer", "argument_1": [0, 2, 3, 4, 5, 6, 7]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "basic_encoder", "step_name": "basic_encoder", "argument_1": [1]}}]
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder),preprocessing2=sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy),algorithm=sklearn.ensemble.forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder),preprocessing2=sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy),algorithm=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "preprocessing", "step_name": "preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "preprocessing2", "step_name": "preprocessing2"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "algorithm", "step_name": "algorithm"}}]
sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy)(1)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "non_binary_imputer", "step_name": "non_binary_imputer", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7]}}]
__main__.SklearnSimpleImputerCloneWithMedianStrategy(1)_copytrue
__main__.SklearnSimpleImputerCloneWithMedianStrategy(1)_fill_valuenull
__main__.SklearnSimpleImputerCloneWithMedianStrategy(1)_missing_valuesNaN
__main__.SklearnSimpleImputerCloneWithMedianStrategy(1)_strategy"median"
__main__.SklearnSimpleImputerCloneWithMedianStrategy(1)_verbose0

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.

17 Evaluation measures

0.9588 ± 0.0016
Per class
Cross-validation details (10-fold Crossvalidation)
0.8955 ± 0.0022
Per class
Cross-validation details (10-fold Crossvalidation)
0.7865 ± 0.0045
Cross-validation details (10-fold Crossvalidation)
30311.5385 ± 22.0061
Cross-validation details (10-fold Crossvalidation)
0.1728 ± 0.0024
Cross-validation details (10-fold Crossvalidation)
0.4886 ± 0
Cross-validation details (10-fold Crossvalidation)
45312
Per class
Cross-validation details (10-fold Crossvalidation)
0.8959 ± 0.0022
Per class
Cross-validation details (10-fold Crossvalidation)
0.8953 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
0.9835
Cross-validation details (10-fold Crossvalidation)
0.8953 ± 0.0022
Per class
Cross-validation details (10-fold Crossvalidation)
0.3536 ± 0.0049
Cross-validation details (10-fold Crossvalidation)
0.4943 ± 0
Cross-validation details (10-fold Crossvalidation)
0.2806 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.5677 ± 0.0055
Cross-validation details (10-fold Crossvalidation)