Run
10560169

Run 10560169

Task 3549 (Supervised Classification) analcatdata_authorship 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_state6317
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_state58151
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

0.9867 ± 0.0088
Per class
Cross-validation details (10-fold Crossvalidation)
0.9452 ± 0.0302
Per class
Cross-validation details (10-fold Crossvalidation)
0.9201 ± 0.0436
Cross-validation details (10-fold Crossvalidation)
0.7275 ± 0.0302
Cross-validation details (10-fold Crossvalidation)
0.1317 ± 0.0114
Cross-validation details (10-fold Crossvalidation)
0.3439 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
0.9453 ± 0.0298
Cross-validation details (10-fold Crossvalidation)
841
Per class
Cross-validation details (10-fold Crossvalidation)
0.9457 ± 0.0293
Per class
Cross-validation details (10-fold Crossvalidation)
0.9453 ± 0.0298
Cross-validation details (10-fold Crossvalidation)
1.7874 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
0.3831 ± 0.0332
Cross-validation details (10-fold Crossvalidation)
0.4146 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
0.2042 ± 0.0166
Cross-validation details (10-fold Crossvalidation)
0.4925 ± 0.0402
Cross-validation details (10-fold Crossvalidation)
0.9312 ± 0.0436
Cross-validation details (10-fold Crossvalidation)