Run
8648

Run 8648

Task 119 (Supervised Classification) diabetes Uploaded 22-01-2024 by Continuous Integration
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TEST75de7e8db4sklearn.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.
TEST75de7e8db4sklearn.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"}}}
TEST75de7e8db4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_error_scoreNaN
TEST75de7e8db4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_iid"deprecated"
TEST75de7e8db4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_n_iter5
TEST75de7e8db4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_n_jobsnull
TEST75de7e8db4sklearn.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]}
TEST75de7e8db4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TEST75de7e8db4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_random_state12172
TEST75de7e8db4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_refittrue
TEST75de7e8db4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_return_train_scorefalse
TEST75de7e8db4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_scoringnull
TEST75de7e8db4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose0
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_bootstraptrue
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_ccp_alpha0.0
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_class_weightnull
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_criterion"gini"
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_max_depthnull
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_max_features"auto"
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_max_leaf_nodesnull
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_max_samplesnull
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_decrease0.0
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_splitnull
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_leaf1
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_split2
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_n_estimators5
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_n_jobsnull
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_oob_scorefalse
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_random_state33003
TEST75de7e8db4sklearn.ensemble._forest.RandomForestClassifier(1)_verbose0
TEST75de7e8db4sklearn.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
0.7849
Per class
F measure
0.7044
Per class
Kappa
0.356
KB relative information score
0.3108
Mean absolute error
0.3128
Mean prior absolute error
0.4589
Weighted recall
0.7075
Number of instances
253
Per class
Precision
0.7028
Per class
Predictive accuracy
0.7075
Prior entropy
0.9463
Relative absolute error
0.6815
Root mean prior squared error
0.4813
Root mean squared error
0.4308
Root relative squared error
0.8952
Unweighted recall
0.6747