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Run 2395

Task 119 (Supervised Classification) diabetes Uploaded 17-10-2024 by Continuous Integration
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TEST38597adedcsklearn.model_selection._search.GridSearchCV(estimator=sklear n.ensemble._forest.RandomForestClassifier)(1)Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and "inverse_transform" if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.
TEST38597adedcsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_cv{"oml-python:serialized_object": "cv_object", "value": {"name": "sklearn.model_selection._split.StratifiedKFold", "parameters": {"n_splits": "2", "random_state": "62501", "shuffle": "true"}}}
TEST38597adedcsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_error_scoreNaN
TEST38597adedcsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_n_jobsnull
TEST38597adedcsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_param_grid[{"max_features": [2, 4]}, {"min_samples_leaf": [1, 10]}]
TEST38597adedcsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TEST38597adedcsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_refittrue
TEST38597adedcsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_return_train_scorefalse
TEST38597adedcsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_scoringnull
TEST38597adedcsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose0
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_bootstraptrue
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_ccp_alpha0.0
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_class_weightnull
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_criterion"gini"
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_max_depthnull
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_max_features"auto"
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_max_leaf_nodesnull
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_max_samplesnull
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_decrease0.0
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_splitnull
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_leaf1
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_split2
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_n_estimators5
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_n_jobsnull
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_oob_scorefalse
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_random_state33003
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_verbose0
TEST38597adedcsklearn.ensemble._forest.RandomForestClassifier(1)_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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

18 Evaluation measures

Area under ROC curve
0.7593
Per class
F measure
0.6945
Per class
Kappa
0.3378
KB relative information score
0.278
Mean absolute error
0.3273
Mean prior absolute error
0.4589
Weighted recall
0.6957
Number of instances
253
Per class
Precision
0.6936
Per class
Predictive accuracy
0.6957
Prior entropy
0.9463
Relative absolute error
0.7131
Root mean prior squared error
0.4813
Root mean squared error
0.4431
Root relative squared error
0.9208
Unweighted recall
0.6677