Run
10461127

Run 10461127

Task 146821 (Supervised Classification) car Uploaded 20-05-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestcla ssifier=sklearn.ensemble.forest.RandomForestClassifier)(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.impute._base.SimpleImputer(11)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(11)_copytrue
sklearn.impute._base.SimpleImputer(11)_fill_valuenull
sklearn.impute._base.SimpleImputer(11)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(11)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(11)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(16)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(16)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(16)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_sparsetrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_verbosefalse
sklearn.ensemble.forest.RandomForestClassifier(64)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(64)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(64)_criterion"entropy"
sklearn.ensemble.forest.RandomForestClassifier(64)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(64)_max_features0.9444876718742468
sklearn.ensemble.forest.RandomForestClassifier(64)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(64)_min_impurity_decrease0
sklearn.ensemble.forest.RandomForestClassifier(64)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(64)_min_samples_leaf4
sklearn.ensemble.forest.RandomForestClassifier(64)_min_samples_split7
sklearn.ensemble.forest.RandomForestClassifier(64)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(64)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(64)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(64)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(64)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(64)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(64)_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.

18 Evaluation measures

Area under ROC curve
0.9973 ± 0.0015
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9688 ± 0.0099
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.932 ± 0.0212
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8909 ± 0.0151
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0334 ± 0.0041
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.229 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9682 ± 0.0103
Cross-validation details (10-fold Crossvalidation)
Number of instances
1728
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9705 ± 0.0091
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9682 ± 0.0103
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.2058 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1461 ± 0.0177
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3381 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1144 ± 0.0102
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3384 ± 0.0298
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9581 ± 0.0241
Cross-validation details (10-fold Crossvalidation)