Run
10464890

Run 10464890

Task 233178 (Supervised Classification) Test_vectors_trans_posneg3 Uploaded 24-06-2020 by Richard Cook
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  • openml-python Sklearn_0.22.2.post1.
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.tree._classes.DecisionTreeClassifier)(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 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.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier)(3)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier)(3)_steps[{"oml-python:serialized_object": "component_reference", "value": {"step_name": "imputer", "key": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"step_name": "estimator", "key": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier)(3)_verbosefalse
sklearn.impute._base.SimpleImputer(14)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(14)_copytrue
sklearn.impute._base.SimpleImputer(14)_fill_valuenull
sklearn.impute._base.SimpleImputer(14)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(14)_strategy"mean"
sklearn.impute._base.SimpleImputer(14)_verbose0
sklearn.tree._classes.DecisionTreeClassifier(4)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(4)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(4)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(4)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(4)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(4)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(4)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(4)_min_impurity_splitnull
sklearn.tree._classes.DecisionTreeClassifier(4)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(4)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(4)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(4)_presort"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(4)_random_state17188
sklearn.tree._classes.DecisionTreeClassifier(4)_splitter"best"

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

Area under ROC curve
0.7129 ± 0.0526
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8931 ± 0.0165
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.486 ± 0.0905
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.4234 ± 0.0807
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0996 ± 0.0139
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2214 ± 0.002
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9004 ± 0.0139
Cross-validation details (10-fold Crossvalidation)
Number of instances
1557
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8904 ± 0.0173
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9004 ± 0.0139
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5478 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4497 ± 0.0628
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3324 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3155 ± 0.0222
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9491 ± 0.0671
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7129 ± 0.0526
Cross-validation details (10-fold Crossvalidation)