Run
10560491

Run 10560491

Task 9985 (Supervised Classification) first-order-theorem-proving Uploaded 14-08-2021 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_state18121
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_state36744
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

0.8211 ± 0.0077
Per class
Cross-validation details (10-fold Crossvalidation)
0.5776 ± 0.012
Per class
Cross-validation details (10-fold Crossvalidation)
0.4388 ± 0.0155
Cross-validation details (10-fold Crossvalidation)
0.409 ± 0.0105
Cross-validation details (10-fold Crossvalidation)
0.1715 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
0.2508 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.5845 ± 0.0119
Cross-validation details (10-fold Crossvalidation)
6118
Per class
Cross-validation details (10-fold Crossvalidation)
0.5745 ± 0.0127
Per class
Cross-validation details (10-fold Crossvalidation)
0.5845 ± 0.0119
Cross-validation details (10-fold Crossvalidation)
2.3 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
0.6839 ± 0.0087
Cross-validation details (10-fold Crossvalidation)
0.3541 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.3081 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
0.87 ± 0.012
Cross-validation details (10-fold Crossvalidation)
0.4736 ± 0.018
Cross-validation details (10-fold Crossvalidation)