Run
9171

Run 9171

Task 119 (Supervised Classification) diabetes Uploaded 25-11-2022 by Continuous Integration
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TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator= sklearn.ensemble.forest.RandomForestClassifier)(1)Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "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 search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(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"}}}
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_error_score"raise"
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_fit_params{}
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_iidtrue
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_iter5
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_jobs1
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_param_distributions{"bootstrap": [true, false], "criterion": ["gini", "entropy"], "max_depth": [3, null], "max_features": [1, 2, 3, 4], "min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10]}
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_random_state12172
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_refittrue
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_return_train_scoretrue
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_scoringnull
TESTad417f5bb9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbose0
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_bootstraptrue
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_class_weightnull
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_criterion"gini"
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_max_depthnull
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_max_features"auto"
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_max_leaf_nodesnull
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_split1e-07
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_leaf1
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_split2
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_n_estimators5
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_n_jobs1
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_oob_scorefalse
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_random_state33003
TESTad417f5bb9sklearn.ensemble.forest.RandomForestClassifier(1)_verbose0
TESTad417f5bb9sklearn.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
0.8246
Per class
F measure
0.7472
Per class
Kappa
0.4446
KB relative information score
0.2855
Mean absolute error
0.336
Mean prior absolute error
0.4589
Weighted recall
0.7549
Number of instances
253
Per class
Precision
0.7498
Per class
Predictive accuracy
0.7549
Prior entropy
0.9463
Relative absolute error
0.7322
Root mean prior squared error
0.4813
Root mean squared error
0.4025
Root relative squared error
0.8364
Unweighted recall
0.712