Run
4905

Run 4905

Task 145 (Supervised Classification) tic-tac-toe Uploaded 05-11-2019 by Test Test
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  • openml-python Sklearn_0.21.2. study_639
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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_state62314
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

0.8301 ± 0.0523
Per class
Cross-validation details (10-fold Crossvalidation)
0.7861 ± 0.0333
Per class
Cross-validation details (10-fold Crossvalidation)
0.5176 ± 0.0783
Cross-validation details (10-fold Crossvalidation)
0.4182 ± 0.0648
Cross-validation details (10-fold Crossvalidation)
0.2631 ± 0.0266
Cross-validation details (10-fold Crossvalidation)
0.453 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
0.7944 ± 0.0279
Cross-validation details (10-fold Crossvalidation)
958
Per class
Cross-validation details (10-fold Crossvalidation)
0.7916 ± 0.0288
Per class
Cross-validation details (10-fold Crossvalidation)
0.7944 ± 0.0279
Cross-validation details (10-fold Crossvalidation)
0.931 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
0.5807 ± 0.0592
Cross-validation details (10-fold Crossvalidation)
0.4759 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.3783 ± 0.0289
Cross-validation details (10-fold Crossvalidation)
0.795 ± 0.0616
Cross-validation details (10-fold Crossvalidation)
0.7436 ± 0.0451
Cross-validation details (10-fold Crossvalidation)