Run
10567527

Run 10567527

Task 3512 (Supervised Classification) synthetic_control Uploaded 01-12-2021 by Marc Boel
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,one hotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclass ifier=sklearn.tree._classes.DecisionTreeClassifier)(2)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final estimator only needs to implement `fit`. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a `'__'`, as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to `'passthrough'` or `None`.
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(2)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}]
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_verbose_feature_names_outtrue
sklearn.impute._base.SimpleImputer(28)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(28)_copytrue
sklearn.impute._base.SimpleImputer(28)_fill_valuenull
sklearn.impute._base.SimpleImputer(28)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(28)_strategy"mean"
sklearn.impute._base.SimpleImputer(28)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(30)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(30)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(30)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(30)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(30)_sparsetrue
sklearn.tree._classes.DecisionTreeClassifier(23)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(23)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(23)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(23)_max_features0.7129948482582983
sklearn.tree._classes.DecisionTreeClassifier(23)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(23)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_leaf13
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_split16
sklearn.tree._classes.DecisionTreeClassifier(23)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_random_state63748
sklearn.tree._classes.DecisionTreeClassifier(23)_splitter"random"

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.

18 Evaluation measures

0.9693 ± 0.0103
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.9450.950.9550.960.9650.970.9750.980.985
0.814 ± 0.0414
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.720.740.760.780.80.820.840.860.880.9
0.778 ± 0.0476
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.680.70.720.740.760.780.80.820.840.860.88
0.8027 ± 0.0292
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.760.770.780.790.80.810.820.830.840.850.860.87
0.0735 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.050.0550.060.0650.070.0750.080.0850.09
0.2778
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.2778
0.815 ± 0.0396
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.740.760.780.80.820.840.860.880.9
600
Per class
Cross-validation details (10-fold Crossvalidation)
0.8168 ± 0.0326
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.780.790.80.810.820.830.840.850.860.870.880.890.9
0.815 ± 0.0396
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.740.760.780.80.820.840.860.880.9
2.585
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore02.585
0.2645 ± 0.0383
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.190.20.210.220.230.240.250.260.270.280.290.30.310.320…0.33
0.3727
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.3727
0.2036 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.1650.170.1750.180.1850.190.1950.20.2050.210.2150.220.2250.…0.23
0.5463 ± 0.0464
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.440.460.480.50.520.540.560.580.60.62
0.815 ± 0.0396
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.740.760.780.80.820.840.860.880.9
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