Run
10559818

Run 10559818

Task 75219 (Supervised Classification) eeg-eye-state Uploaded 15-03-2021 by Marvin Mavril
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.tree._classes.DecisionTreeClassifier)(9)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._classes.DecisionTreeClassifier(12)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(12)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(12)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(12)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(12)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(12)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(12)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(12)_min_impurity_splitnull
sklearn.tree._classes.DecisionTreeClassifier(12)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(12)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(12)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(12)_presort"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(12)_random_state3680
sklearn.tree._classes.DecisionTreeClassifier(12)_splitter"best"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier)(9)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier)(9)_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._classes.DecisionTreeClassifier)(9)_verbosefalse
sklearn.impute._base.SimpleImputer(21)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(21)_copytrue
sklearn.impute._base.SimpleImputer(21)_fill_valuenull
sklearn.impute._base.SimpleImputer(21)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(21)_strategy"mean"
sklearn.impute._base.SimpleImputer(21)_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

Area under ROC curve
0.8233
Per class
Cross-validation details (33% Holdout set)
F measure
0.8253
Per class
Cross-validation details (33% Holdout set)
Kappa
0.6472
Cross-validation details (33% Holdout set)
KB relative information score
0.6457
Cross-validation details (33% Holdout set)
Mean absolute error
0.1746
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.495
Cross-validation details (33% Holdout set)
Weighted recall
0.8254
Cross-validation details (33% Holdout set)
Number of instances
4943
Per class
Cross-validation details (33% Holdout set)
Precision
0.8253
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.8254
Cross-validation details (33% Holdout set)
Prior entropy
0.9932
Cross-validation details (33% Holdout set)
Relative absolute error
0.3527
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.4976
Cross-validation details (33% Holdout set)
Root mean squared error
0.4178
Cross-validation details (33% Holdout set)
Root relative squared error
0.8397
Cross-validation details (33% Holdout set)
Unweighted recall
0.8233
Cross-validation details (33% Holdout set)