Run
6122877

Run 6122877

Task 3950 (Supervised Classification) musk Uploaded 18-08-2017 by Jan van Rijn
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeli ne.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hoten coding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.f eature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.en semble.forest.RandomForestClassifier))(2)Automatically created scikit-learn flow.
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_cvnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_error_score"raise"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_fit_params{}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_iidtrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_n_iter50
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_n_jobs1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_param_distributions{"classifier__min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__bootstrap": [true, false], "classifier__min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__criterion": ["gini", "entropy"], "imputation__strategy": ["mean", "median", "most_frequent"]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_random_state1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_refittrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_return_train_scoretrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_scoringnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_verbose0
openmlstudy14.preprocessing.ConditionalImputer(4)_axis0
openmlstudy14.preprocessing.ConditionalImputer(4)_categorical_features[0]
openmlstudy14.preprocessing.ConditionalImputer(4)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(4)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(4)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(4)_strategy"median"
openmlstudy14.preprocessing.ConditionalImputer(4)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(4)_verbose0
sklearn.preprocessing.data.OneHotEncoder(8)_categorical_features[0]
sklearn.preprocessing.data.OneHotEncoder(8)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(8)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(8)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(8)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(6)_threshold0.0
sklearn.ensemble.forest.RandomForestClassifier(23)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(23)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(23)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(23)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(23)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(23)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(23)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(23)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(23)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(23)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(23)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(23)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(23)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(23)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(23)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(23)_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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

17 Evaluation measures

Area under ROC curve
0.9994 ± 0.0008
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9913 ± 0.0031
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9664 ± 0.0119
Cross-validation details (10-fold Crossvalidation)
KB relative information score
5852.6355 ± 23.2667
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0325 ± 0.0075
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2608 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Number of instances
6598
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9914 ± 0.0031
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9914 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.6204
Cross-validation details (10-fold Crossvalidation)
Recall
0.9914 ± 0.003
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1245 ± 0.0289
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3611 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0952 ± 0.0159
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.2637 ± 0.0441
Cross-validation details (10-fold Crossvalidation)