Run
143

Run 143

Task 119 (Supervised Classification) diabetes Uploaded 17-10-2024 by Continuous Integration
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  • openml-python Sklearn_1.4.2. study_250 study_368
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Flow

TEST1de4e56c87sklearn.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.
TEST1de4e56c87sklearn.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"}}}
TEST1de4e56c87sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_error_scoreNaN
TEST1de4e56c87sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_n_jobsnull
TEST1de4e56c87sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_param_grid[{"max_features": [2, 4]}, {"min_samples_leaf": [1, 10]}]
TEST1de4e56c87sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TEST1de4e56c87sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_refittrue
TEST1de4e56c87sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_return_train_scorefalse
TEST1de4e56c87sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_scoringnull
TEST1de4e56c87sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose0
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_bootstraptrue
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_ccp_alpha0.0
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_class_weightnull
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_criterion"gini"
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_max_depthnull
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_max_features"sqrt"
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_max_leaf_nodesnull
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_max_samplesnull
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_decrease0.0
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_leaf1
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_split2
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_monotonic_cstnull
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_n_estimators5
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_n_jobsnull
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_oob_scorefalse
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_random_state33003
TEST1de4e56c87sklearn.ensemble._forest.RandomForestClassifier(1)_verbose0
TEST1de4e56c87sklearn.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