Run
10560419

Run 10560419

Task 167141 (Supervised Classification) churn Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestcla ssifier=sklearn.ensemble.forest.RandomForestClassifier)(4)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 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 to None.
sklearn.decomposition.pca.PCA(11)_copytrue
sklearn.decomposition.pca.PCA(11)_iterated_power"auto"
sklearn.decomposition.pca.PCA(11)_n_componentsnull
sklearn.decomposition.pca.PCA(11)_random_state31029
sklearn.decomposition.pca.PCA(11)_svd_solver"auto"
sklearn.decomposition.pca.PCA(11)_tol0.0
sklearn.decomposition.pca.PCA(11)_whitenfalse
sklearn.ensemble.forest.RandomForestClassifier(67)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(67)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(67)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(67)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(67)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(67)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(67)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(67)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(67)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(67)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(67)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(67)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(67)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(67)_random_state23379
sklearn.ensemble.forest.RandomForestClassifier(67)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(67)_warm_startfalse
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "pca", "step_name": "pca"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]

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

0.9059 ± 0.033
Per class
Cross-validation details (10-fold Crossvalidation)
0.9131 ± 0.0179
Per class
Cross-validation details (10-fold Crossvalidation)
0.6112 ± 0.084
Cross-validation details (10-fold Crossvalidation)
0.34 ± 0.0665
Cross-validation details (10-fold Crossvalidation)
0.1384 ± 0.0118
Cross-validation details (10-fold Crossvalidation)
0.2429 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.922 ± 0.0146
Cross-validation details (10-fold Crossvalidation)
5000
Per class
Cross-validation details (10-fold Crossvalidation)
0.9196 ± 0.0175
Per class
Cross-validation details (10-fold Crossvalidation)
0.922 ± 0.0146
Cross-validation details (10-fold Crossvalidation)
0.5879 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
0.5697 ± 0.0485
Cross-validation details (10-fold Crossvalidation)
0.3484 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.2474 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
0.7101 ± 0.0535
Cross-validation details (10-fold Crossvalidation)
0.7525 ± 0.041
Cross-validation details (10-fold Crossvalidation)