Run
10560248

Run 10560248

Task 32 (Supervised Classification) pendigits 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_state5745
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

0.9993 ± 0.0005
Per class
Cross-validation details (10-fold Crossvalidation)
0.988 ± 0.0045
Per class
Cross-validation details (10-fold Crossvalidation)
0.9867 ± 0.0051
Cross-validation details (10-fold Crossvalidation)
0.9668 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
0.0118 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
0.18 ± 0
Cross-validation details (10-fold Crossvalidation)
0.988 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
10992
Per class
Cross-validation details (10-fold Crossvalidation)
0.988 ± 0.0045
Per class
Cross-validation details (10-fold Crossvalidation)
0.988 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
3.3208 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.0656 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
0.3 ± 0
Cross-validation details (10-fold Crossvalidation)
0.0581 ± 0.004
Cross-validation details (10-fold Crossvalidation)
0.1937 ± 0.0133
Cross-validation details (10-fold Crossvalidation)
0.9881 ± 0.0045
Cross-validation details (10-fold Crossvalidation)