Run
10457551

Run 10457551

Task 3797 (Supervised Classification) socmob Uploaded 19-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.mu lti_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.im pute._knn.KNNImputer,step_2=sklearn.ensemble._hist_gradient_boosting.gradie nt_boosting.HistGradientBoostingClassifier)(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.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization0.3348075347729901
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.020136733749861895
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins69
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth4
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter878
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes474
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf143
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change50
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"jaccard_macro"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.20168522128991237
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.43911209662202855
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.impute._knn.KNNImputer(1)_add_indicatortrue
sklearn.impute._knn.KNNImputer(1)_copyfalse
sklearn.impute._knn.KNNImputer(1)_metric"nan_euclidean"
sklearn.impute._knn.KNNImputer(1)_missing_valuesNaN
sklearn.impute._knn.KNNImputer(1)_n_neighbors13
sklearn.impute._knn.KNNImputer(1)_weights"uniform"
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(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

0.9637 ± 0.0082
Per class
Cross-validation details (10-fold Crossvalidation)
0.9213 ± 0.0201
Per class
Cross-validation details (10-fold Crossvalidation)
0.7776 ± 0.0557
Cross-validation details (10-fold Crossvalidation)
0.5266 ± 0.0412
Cross-validation details (10-fold Crossvalidation)
0.1767 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
0.3451 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
0.9196 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
1156
Per class
Cross-validation details (10-fold Crossvalidation)
0.9252 ± 0.0198
Per class
Cross-validation details (10-fold Crossvalidation)
0.9196 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
0.7628 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
0.5119 ± 0.0273
Cross-validation details (10-fold Crossvalidation)
0.4152 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.2605 ± 0.0177
Cross-validation details (10-fold Crossvalidation)
0.6275 ± 0.0415
Cross-validation details (10-fold Crossvalidation)
0.9078 ± 0.0312
Cross-validation details (10-fold Crossvalidation)