Run
10560338

Run 10560338

Task 53 (Supervised Classification) vehicle Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, estimator=sklearn.ensemble.forest.RandomForestClassifier)(7)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_state34100
sklearn.ensemble.forest.RandomForestClassifier(67)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(67)_warm_startfalse
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
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.ensemble.forest.RandomForestClassifier)(7)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]

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

0.9062 ± 0.0142
Per class
Cross-validation details (10-fold Crossvalidation)
0.7128 ± 0.0373
Per class
Cross-validation details (10-fold Crossvalidation)
0.6234 ± 0.0484
Cross-validation details (10-fold Crossvalidation)
0.6338 ± 0.024
Cross-validation details (10-fold Crossvalidation)
0.1683 ± 0.0085
Cross-validation details (10-fold Crossvalidation)
0.3748 ± 0
Cross-validation details (10-fold Crossvalidation)
0.7175 ± 0.0363
Cross-validation details (10-fold Crossvalidation)
846
Per class
Cross-validation details (10-fold Crossvalidation)
0.7096 ± 0.0394
Per class
Cross-validation details (10-fold Crossvalidation)
0.7175 ± 0.0363
Cross-validation details (10-fold Crossvalidation)
1.9991 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.449 ± 0.0226
Cross-validation details (10-fold Crossvalidation)
0.4329 ± 0
Cross-validation details (10-fold Crossvalidation)
0.2956 ± 0.0129
Cross-validation details (10-fold Crossvalidation)
0.6827 ± 0.0299
Cross-validation details (10-fold Crossvalidation)
0.7208 ± 0.0364
Cross-validation details (10-fold Crossvalidation)