OpenML
10594062

Run 10594062

Task 52950 (Supervised Classification) higgs Uploaded 29-08-2023 by Matthias Schartner
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,scaler =sklearn.preprocessing._data.StandardScaler,clf=sklearn.ensemble._forest.Ra ndomForestClassifier)(1)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(50)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(50)_copytrue
sklearn.impute._base.SimpleImputer(50)_fill_valuenull
sklearn.impute._base.SimpleImputer(50)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(50)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(50)_strategy"mean"
sklearn.preprocessing._data.StandardScaler(19)_copytrue
sklearn.preprocessing._data.StandardScaler(19)_with_meantrue
sklearn.preprocessing._data.StandardScaler(19)_with_stdtrue
sklearn.ensemble._forest.RandomForestClassifier(34)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(34)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(34)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(34)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(34)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(34)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(34)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(34)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(34)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(34)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_n_estimators1000
sklearn.ensemble._forest.RandomForestClassifier(34)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(34)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(34)_random_state24444
sklearn.ensemble._forest.RandomForestClassifier(34)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(34)_warm_startfalse
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler,clf=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler,clf=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "clf", "step_name": "clf"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler,clf=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbosefalse

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
0.8027 ± 0.0063
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7241 ± 0.0048
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4461 ± 0.0096
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2423 ± 0.0051
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.393 ± 0.0021
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4984 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7243 ± 0.0047
Cross-validation details (10-fold Crossvalidation)
Number of instances
98050
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.724 ± 0.0048
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7243 ± 0.0047
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9976 ± 0
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7885 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4992 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4298 ± 0.0024
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8609 ± 0.0048
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7228 ± 0.0048
Cross-validation details (10-fold Crossvalidation)