Run
10559683

Run 10559683

Task 317602 (Supervised Classification) kr-vs-kp Uploaded 30-09-2020 by ray wright
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier) (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.impute._base.SimpleImputer(16)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(16)_copytrue
sklearn.impute._base.SimpleImputer(16)_fill_valuenull
sklearn.impute._base.SimpleImputer(16)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(16)_strategy"mean"
sklearn.impute._base.SimpleImputer(16)_verbose0
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier)(1)_verbosefalse
sklearn.ensemble._gb.GradientBoostingClassifier(2)_ccp_alpha0.0
sklearn.ensemble._gb.GradientBoostingClassifier(2)_criterion"friedman_mse"
sklearn.ensemble._gb.GradientBoostingClassifier(2)_initnull
sklearn.ensemble._gb.GradientBoostingClassifier(2)_learning_rate0.001
sklearn.ensemble._gb.GradientBoostingClassifier(2)_loss"deviance"
sklearn.ensemble._gb.GradientBoostingClassifier(2)_max_depth3
sklearn.ensemble._gb.GradientBoostingClassifier(2)_max_featuresnull
sklearn.ensemble._gb.GradientBoostingClassifier(2)_max_leaf_nodesnull
sklearn.ensemble._gb.GradientBoostingClassifier(2)_min_impurity_decrease0.0
sklearn.ensemble._gb.GradientBoostingClassifier(2)_min_impurity_splitnull
sklearn.ensemble._gb.GradientBoostingClassifier(2)_min_samples_leaf1
sklearn.ensemble._gb.GradientBoostingClassifier(2)_min_samples_split2
sklearn.ensemble._gb.GradientBoostingClassifier(2)_min_weight_fraction_leaf0.0
sklearn.ensemble._gb.GradientBoostingClassifier(2)_n_estimators100
sklearn.ensemble._gb.GradientBoostingClassifier(2)_n_iter_no_changenull
sklearn.ensemble._gb.GradientBoostingClassifier(2)_presort"deprecated"
sklearn.ensemble._gb.GradientBoostingClassifier(2)_random_state4161
sklearn.ensemble._gb.GradientBoostingClassifier(2)_subsample1.0
sklearn.ensemble._gb.GradientBoostingClassifier(2)_tol0.0001
sklearn.ensemble._gb.GradientBoostingClassifier(2)_validation_fraction0.1
sklearn.ensemble._gb.GradientBoostingClassifier(2)_verbose0
sklearn.ensemble._gb.GradientBoostingClassifier(2)_warm_startfalse

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
0.9386
Per class
Cross-validation details (33% Holdout set)
F measure
0.9052
Per class
Cross-validation details (33% Holdout set)
Kappa
0.8094
Cross-validation details (33% Holdout set)
KB relative information score
0.0903
Cross-validation details (33% Holdout set)
Mean absolute error
0.4662
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.4984
Cross-validation details (33% Holdout set)
Weighted recall
0.9061
Cross-validation details (33% Holdout set)
Number of instances
1054
Per class
Cross-validation details (33% Holdout set)
Precision
0.9128
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.9061
Cross-validation details (33% Holdout set)
Prior entropy
0.9969
Cross-validation details (33% Holdout set)
Relative absolute error
0.9353
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.4989
Cross-validation details (33% Holdout set)
Root mean squared error
0.467
Cross-validation details (33% Holdout set)
Root relative squared error
0.9359
Cross-validation details (33% Holdout set)
Unweighted recall
0.9009
Cross-validation details (33% Holdout set)