Run
9200467

Run 9200467

Task 60 (Supervised Classification) zoo Uploaded 06-05-2018 by Benjamin Strang
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  • openml-python Sklearn_0.19.1. study_123
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Flow

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeli ne.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one- hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding= sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scal ing=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classe s.LinearSVC))(1)Automatically created scikit-learn flow.
mylib.preprocessing_openml14.ConditionalImputer(1)_axis0
mylib.preprocessing_openml14.ConditionalImputer(1)_categorical_features[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15]
mylib.preprocessing_openml14.ConditionalImputer(1)_copytrue
mylib.preprocessing_openml14.ConditionalImputer(1)_fill_empty0
mylib.preprocessing_openml14.ConditionalImputer(1)_missing_values"NaN"
mylib.preprocessing_openml14.ConditionalImputer(1)_strategy"median"
mylib.preprocessing_openml14.ConditionalImputer(1)_strategy_nominal"most_frequent"
mylib.preprocessing_openml14.ConditionalImputer(1)_verbose0
sklearn.preprocessing.data.OneHotEncoder(17)_categorical_features[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15]
sklearn.preprocessing.data.OneHotEncoder(17)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(17)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(17)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(17)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(11)_threshold0.0
sklearn.preprocessing.data.StandardScaler(5)_copytrue
sklearn.preprocessing.data.StandardScaler(5)_with_meanfalse
sklearn.preprocessing.data.StandardScaler(5)_with_stdtrue
sklearn.svm.classes.LinearSVC(5)_C1.0
sklearn.svm.classes.LinearSVC(5)_class_weightnull
sklearn.svm.classes.LinearSVC(5)_dualtrue
sklearn.svm.classes.LinearSVC(5)_fit_intercepttrue
sklearn.svm.classes.LinearSVC(5)_intercept_scaling1
sklearn.svm.classes.LinearSVC(5)_loss"squared_hinge"
sklearn.svm.classes.LinearSVC(5)_max_iter10000
sklearn.svm.classes.LinearSVC(5)_multi_class"ovr"
sklearn.svm.classes.LinearSVC(5)_penalty"l2"
sklearn.svm.classes.LinearSVC(5)_random_state48903
sklearn.svm.classes.LinearSVC(5)_tol0.0001
sklearn.svm.classes.LinearSVC(5)_verbose0
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_cv3
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_error_score"raise"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_fit_paramsnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_iidtrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_n_iter250
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_n_jobs-1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_param_distributions{"classifier__C": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "mylib.distributions.loguniform_gen", "a": 0.03125, "b": 32768, "args": [], "kwds": {"base": 2, "low": 0.03125, "high": 32768}}}, "classifier__dual": [true, false], "classifier__tol": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "mylib.distributions.loguniform_gen", "a": 1e-05, "b": 0.1, "args": [], "kwds": {"base": 10, "low": 1e-05, "high": 0.1}}}, "imputation__strategy": ["mean", "median", "most_frequent"]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_random_state1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_refittrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_return_train_score"warn"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_scoringnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC))(1)_verbose0
sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC)(1)_memorynull

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.

17 Evaluation measures

Area under ROC curve
0.9724 ± 0.0296
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9503
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9347 ± 0.0697
Cross-validation details (10-fold Crossvalidation)
KB relative information score
90.6733 ± 0.8874
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0141 ± 0.0151
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2188 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Number of instances
101
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9528
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9505 ± 0.0527
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.4427
Cross-validation details (10-fold Crossvalidation)
Recall
0.9505 ± 0.0527
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0647 ± 0.0686
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3294 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1189 ± 0.0891
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.361 ± 0.2696
Cross-validation details (10-fold Crossvalidation)