Run
10457621

Run 10457621

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.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._uni variate_selection.SelectPercentile,step_2=sklearn.ensemble._hist_gradient_b oosting.gradient_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_regularization0.0018678062431716463
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.5369326454485449
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins178
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth20
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter12
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes1117
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf18
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change20
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"balanced_accuracy"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.16205721638234674
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.4592114705505216
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_percentile22.800696349366373
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_score_func{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._mutual_info.mutual_info_classif"}
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,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.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,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

Area under ROC curve
0.9513 ± 0.0184
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.92 ± 0.0197
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7666 ± 0.0567
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6407 ± 0.0448
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1164 ± 0.0119
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3451 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9204 ± 0.0198
Cross-validation details (10-fold Crossvalidation)
Number of instances
1156
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9196 ± 0.0199
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9204 ± 0.0198
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7628 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3373 ± 0.0346
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4152 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2529 ± 0.0205
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.609 ± 0.0488
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.879 ± 0.0295
Cross-validation details (10-fold Crossvalidation)