Run
10560387

Run 10560387

Task 9952 (Supervised Classification) phoneme Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(3)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.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_state59603
sklearn.ensemble.forest.RandomForestClassifier(67)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(67)_warm_startfalse
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(3)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.preprocessing.imputation.Imputer(52)_axis0
sklearn.preprocessing.imputation.Imputer(52)_copytrue
sklearn.preprocessing.imputation.Imputer(52)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(52)_strategy"median"
sklearn.preprocessing.imputation.Imputer(52)_verbose0

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

0.9505 ± 0.0093
Per class
Cross-validation details (10-fold Crossvalidation)
0.8983 ± 0.0111
Per class
Cross-validation details (10-fold Crossvalidation)
0.7518 ± 0.0275
Cross-validation details (10-fold Crossvalidation)
0.635 ± 0.0204
Cross-validation details (10-fold Crossvalidation)
0.154 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
0.4147 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.8997 ± 0.0108
Cross-validation details (10-fold Crossvalidation)
5404
Per class
Cross-validation details (10-fold Crossvalidation)
0.8983 ± 0.0113
Per class
Cross-validation details (10-fold Crossvalidation)
0.8997 ± 0.0108
Cross-validation details (10-fold Crossvalidation)
0.8732 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.3714 ± 0.0176
Cross-validation details (10-fold Crossvalidation)
0.4554 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.2738 ± 0.0115
Cross-validation details (10-fold Crossvalidation)
0.6012 ± 0.0252
Cross-validation details (10-fold Crossvalidation)
0.8664 ± 0.0156
Cross-validation details (10-fold Crossvalidation)