Run
10560425

Run 10560425

Task 167119 (Supervised Classification) jungle_chess_2pcs_raw_endgame_complete 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_state63350
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_state49901
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

0.9558 ± 0.0054
Per class
Cross-validation details (10-fold Crossvalidation)
0.8389 ± 0.0155
Per class
Cross-validation details (10-fold Crossvalidation)
0.7196 ± 0.027
Cross-validation details (10-fold Crossvalidation)
0.7012 ± 0.0144
Cross-validation details (10-fold Crossvalidation)
0.1295 ± 0.0048
Cross-validation details (10-fold Crossvalidation)
0.3832 ± 0
Cross-validation details (10-fold Crossvalidation)
0.8406 ± 0.0153
Cross-validation details (10-fold Crossvalidation)
44819
Per class
Cross-validation details (10-fold Crossvalidation)
0.8383 ± 0.0156
Per class
Cross-validation details (10-fold Crossvalidation)
0.8406 ± 0.0153
Cross-validation details (10-fold Crossvalidation)
1.3491 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.3378 ± 0.0126
Cross-validation details (10-fold Crossvalidation)
0.4377 ± 0
Cross-validation details (10-fold Crossvalidation)
0.2636 ± 0.01
Cross-validation details (10-fold Crossvalidation)
0.6022 ± 0.0228
Cross-validation details (10-fold Crossvalidation)
0.7741 ± 0.0203
Cross-validation details (10-fold Crossvalidation)