Run
10562084

Run 10562084

Task 21 (Supervised Classification) car Uploaded 30-11-2021 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"most_frequent"
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_features0.5668526383381972
sklearn.tree._classes.DecisionTreeClassifier(23)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(23)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_leaf11
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(23)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_random_state62446
sklearn.tree._classes.DecisionTreeClassifier(23)_splitter"best"

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

0.9864 ± 0.0048
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.970.9750.980.9850.990.9…0.995
0.9139 ± 0.0211
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.880.90.920.940.960.86
0.812 ± 0.046
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.7250.750.7750.80.8250.850.8750.9
0.8184 ± 0.0354
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.80.750.7750.8250.850.8…0.875
0.0502 ± 0.0087
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.0350.040.0450.050.0550.060.0650.…0.07
0.229 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.227750.2280.228250.22850.228750.2290.229250.22950.22975
0.9126 ± 0.0214
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.860.880.90.920.940.96
1728
Per class
Cross-validation details (10-fold Crossvalidation)
0.9156 ± 0.0221
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.880.90.920.940.960.98
0.9126 ± 0.0214
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.860.880.90.920.940.96
1.2058 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore01.191.1951.21.2051.211.2151.22
0.2192 ± 0.0378
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.150.1750.20.2250.250.2750.3
0.3381 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.33650.3370.33750.3380.33850.3390.…0.3395
0.1643 ± 0.0162
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.130.140.150.160.170.180.190.2
0.4859 ± 0.0475
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.40.450.50.550.350.6
0.8418 ± 0.0505
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.750.80.850.90.95
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