Run
10154828

Run 10154828

Task 3 (Supervised Classification) kr-vs-kp Uploaded 21-02-2019 by Pieter Gijsbers
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Flow

sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer .ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=categor y_encoders.target_encoder.TargetEncoder),algorithm=sklearn.ensemble.forest. RandomForestClassifier)(1)Automatically created scikit-learn flow.
sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder)(2)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder)(2)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder)(2)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder)(2)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder)(2)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer", "argument_1": []}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat_encoder", "step_name": "cat_encoder", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]}}]
sklearn.impute.SimpleImputer(8)_copytrue
sklearn.impute.SimpleImputer(8)_fill_valuenull
sklearn.impute.SimpleImputer(8)_missing_valuesNaN
sklearn.impute.SimpleImputer(8)_strategy"mean"
sklearn.impute.SimpleImputer(8)_verbose0
category_encoders.target_encoder.TargetEncoder(3)_cols[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
category_encoders.target_encoder.TargetEncoder(3)_drop_invariantfalse
category_encoders.target_encoder.TargetEncoder(3)_handle_unknown"impute"
category_encoders.target_encoder.TargetEncoder(3)_impute_missingtrue
category_encoders.target_encoder.TargetEncoder(3)_min_samples_leaf1
category_encoders.target_encoder.TargetEncoder(3)_return_dftrue
category_encoders.target_encoder.TargetEncoder(3)_smoothing1.0
category_encoders.target_encoder.TargetEncoder(3)_verbose0
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder),algorithm=sklearn.ensemble.forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder),algorithm=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "preprocessing", "step_name": "preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "algorithm", "step_name": "algorithm"}}]
sklearn.ensemble.forest.RandomForestClassifier(49)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(49)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(49)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(49)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(49)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(49)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(49)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(49)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(49)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(49)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(49)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(49)_n_estimators"warn"
sklearn.ensemble.forest.RandomForestClassifier(49)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(49)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(49)_random_state9550
sklearn.ensemble.forest.RandomForestClassifier(49)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(49)_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
0.9981 ± 0.0017
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9834 ± 0.009
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9667 ± 0.018
Cross-validation details (10-fold Crossvalidation)
KB relative information score
2892.1227 ± 3.3897
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0555 ± 0.005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.499 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
3196
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9835 ± 0.0087
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9834 ± 0.009
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9986
Cross-validation details (10-fold Crossvalidation)
Recall
0.9834 ± 0.009
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1112 ± 0.01
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4995 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1271 ± 0.0155
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.2544 ± 0.0311
Cross-validation details (10-fold Crossvalidation)