Run
10459825

Run 10459825

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 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=sklearn.impute._knn.KNNImputer,step_1=skle arn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoosting Classifier)(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_regularization0.005706713453486847
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.0005889584327599946
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins207
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth5
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter727
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes3049
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf6470
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change18
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"precision_weighted"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.16369256488884099
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.47946428185248613
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.impute._knn.KNNImputer(1)_add_indicatorfalse
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_neighbors21
sklearn.impute._knn.KNNImputer(1)_weights"uniform"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,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=sklearn.impute._knn.KNNImputer,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.

16 Evaluation measures

Area under ROC curve
0.5
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4271 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4271 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6909 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Number of instances
16599
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6909 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8921 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4621 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4621 ± 0
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5
Cross-validation details (10-fold Crossvalidation)