Run
10567557

Run 10567557

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"median"
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_features0.30856636798777515
sklearn.tree._classes.DecisionTreeClassifier(23)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(23)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_split6
sklearn.tree._classes.DecisionTreeClassifier(23)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_random_state40339
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

0.9433 ± 0.0193
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.90.910.920.930.940.950.960.970.98
0.8722 ± 0.0435
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.740.760.780.80.820.840.860.880.90.920.94
0.848 ± 0.0509
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.70.7250.750.7750.80.8250.850.8750.90.925
0.8624 ± 0.0374
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.760.780.80.820.840.860.880.90.92
0.0449 ± 0.0114
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.030.0350.040.0450.050.0550.060.0650.070.075
0.2778
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.2778
0.8733 ± 0.0425
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.740.760.780.80.820.840.860.880.90.920.94
600
Per class
Cross-validation details (10-fold Crossvalidation)
0.8757 ± 0.0434
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.760.780.80.820.840.860.880.90.920.940.96
0.8733 ± 0.0425
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.740.760.780.80.820.840.860.880.90.920.94
2.585
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore02.585
0.1616 ± 0.041
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.10.120.140.160.180.20.220.240.260.28
0.3727
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.3727
0.1935 ± 0.027
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.150.160.170.180.190.20.210.220.230.240.250.26
0.5191 ± 0.0724
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.40.4250.450.4750.50.5250.550.5750.60.6250.650.6750.7
0.8733 ± 0.0425
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.740.760.780.80.820.840.860.880.90.920.94
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