Run
10587779

Run 10587779

Task 189871 (Supervised Classification) dilbert Uploaded 01-06-2022 by Jonathan McKinney
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(2)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.impute._base.SimpleImputer(25)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(25)_copytrue
sklearn.impute._base.SimpleImputer(25)_fill_valuenull
sklearn.impute._base.SimpleImputer(25)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(25)_strategy"mean"
sklearn.impute._base.SimpleImputer(25)_verbose0
sklearn.tree._classes.DecisionTreeClassifier(20)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(20)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(20)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(20)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(20)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(20)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(20)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(20)_min_impurity_splitnull
sklearn.tree._classes.DecisionTreeClassifier(20)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(20)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(20)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(20)_random_state39874
sklearn.tree._classes.DecisionTreeClassifier(20)_splitter"best"
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(2)_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

Area under ROC curve
0.8658
Per class
Cross-validation details (33% Holdout set)
F measure
0.7853
Per class
Cross-validation details (33% Holdout set)
Kappa
0.7305
Cross-validation details (33% Holdout set)
KB relative information score
0.7543
Cross-validation details (33% Holdout set)
Mean absolute error
0.0862
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.3199
Cross-validation details (33% Holdout set)
Weighted recall
0.7845
Cross-validation details (33% Holdout set)
Number of instances
3300
Per class
Cross-validation details (33% Holdout set)
Precision
0.7866
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.7845
Cross-validation details (33% Holdout set)
Prior entropy
2.3205
Cross-validation details (33% Holdout set)
Relative absolute error
0.2694
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.3999
Cross-validation details (33% Holdout set)
Root mean squared error
0.2936
Cross-validation details (33% Holdout set)
Root relative squared error
0.7341
Cross-validation details (33% Holdout set)
Unweighted recall
0.7822
Cross-validation details (33% Holdout set)