Run
10456737

Run 10456737

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.en semble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassi fier)(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.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=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.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"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=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
0.9637 ± 0.0082
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9213 ± 0.0201
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7776 ± 0.0557
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5266 ± 0.0412
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1767 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3451 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9196 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
Number of instances
1156
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9252 ± 0.0198
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9196 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7628 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.5119 ± 0.0273
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
0.2605 ± 0.0177
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6275 ± 0.0415
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9078 ± 0.0312
Cross-validation details (10-fold Crossvalidation)