Run
8870

Run 8870

Task 589 (Supervised Classification) texture Uploaded 06-11-2019 by Test Test
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  • openml-python Sklearn_0.21.2. study_1164
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.tree.tree.DecisionTreeClassifier)(1)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 transformers in the pipeline can be cached using ``memory`` argument. 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 it to 'passthrough' or ``None``.
sklearn.tree.tree.DecisionTreeClassifier(1)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(1)_max_depth5
sklearn.tree.tree.DecisionTreeClassifier(1)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(1)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(1)_random_state34319
sklearn.tree.tree.DecisionTreeClassifier(1)_splitter"best"
sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(1)_copytrue
sklearn.impute._base.SimpleImputer(1)_fill_valuenull
sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(1)_strategy"mean"
sklearn.impute._base.SimpleImputer(1)_verbose0
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_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"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbosefalse

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

0.9253 ± 0.0037
Per class
Cross-validation details (10-fold Crossvalidation)
0.5894 ± 0.0162
Per class
Cross-validation details (10-fold Crossvalidation)
0.6094 ± 0.0116
Cross-validation details (10-fold Crossvalidation)
0.6552 ± 0.0096
Cross-validation details (10-fold Crossvalidation)
0.0782 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
0.1653
Cross-validation details (10-fold Crossvalidation)
0.6449 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
5500
Per class
Cross-validation details (10-fold Crossvalidation)
0.6703 ± 0.0238
Per class
Cross-validation details (10-fold Crossvalidation)
0.6449 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
3.4594
Cross-validation details (10-fold Crossvalidation)
0.4728 ± 0.0101
Cross-validation details (10-fold Crossvalidation)
0.2875
Cross-validation details (10-fold Crossvalidation)
0.1995 ± 0.0028
Cross-validation details (10-fold Crossvalidation)
0.6941 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
0.6449 ± 0.0106
Cross-validation details (10-fold Crossvalidation)