Run
10010713

Run 10010713

Task 36 (Supervised Classification) segment Uploaded 18-01-2019 by Scikit-learn Bot
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.pr eprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.St andardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.imput e.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder )),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceT hreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassif ier)(2)Automatically created scikit-learn flow.
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_transformer_weightsnull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(3)_memorynull
sklearn.preprocessing.imputation.Imputer(34)_axis0
sklearn.preprocessing.imputation.Imputer(34)_copytrue
sklearn.preprocessing.imputation.Imputer(34)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(34)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(34)_verbose0
sklearn.preprocessing.data.StandardScaler(20)_copytrue
sklearn.preprocessing.data.StandardScaler(20)_with_meantrue
sklearn.preprocessing.data.StandardScaler(20)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_memorynull
sklearn.impute.SimpleImputer(6)_copytrue
sklearn.impute.SimpleImputer(6)_fill_value-1
sklearn.impute.SimpleImputer(6)_missing_valuesNaN
sklearn.impute.SimpleImputer(6)_strategy"constant"
sklearn.impute.SimpleImputer(6)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(6)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(6)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(6)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(6)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(6)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(6)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(21)_threshold0.0
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_memorynull
sklearn.ensemble.forest.RandomForestClassifier(48)_bootstrapfalse
sklearn.ensemble.forest.RandomForestClassifier(48)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(48)_criterion"entropy"
sklearn.ensemble.forest.RandomForestClassifier(48)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(48)_max_features0.2664628106279351
sklearn.ensemble.forest.RandomForestClassifier(48)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(48)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(48)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(48)_min_samples_leaf15
sklearn.ensemble.forest.RandomForestClassifier(48)_min_samples_split4
sklearn.ensemble.forest.RandomForestClassifier(48)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(48)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(48)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(48)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(48)_random_state22827
sklearn.ensemble.forest.RandomForestClassifier(48)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(48)_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.

17 Evaluation measures

Area under ROC curve
0.9987 ± 0.0006
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9684 ± 0.0113
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9631 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
KB relative information score
2142.5933 ± 2.7687
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0305 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2449
Cross-validation details (10-fold Crossvalidation)
Number of instances
2310
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9684 ± 0.011
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9684 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.8074
Cross-validation details (10-fold Crossvalidation)
Recall
0.9684 ± 0.0112
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1246 ± 0.0171
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3499
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0947 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.2706 ± 0.0282
Cross-validation details (10-fold Crossvalidation)