Run
162

Run 162

Task 119 (Supervised Classification) diabetes Uploaded 29-10-2019 by Continuous Integration
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TESTa25428c330sklearn.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. Note that before SciPy 0.16, the ``scipy.stats.distributions`` do not accept a custom RNG instance and always use the singleton RNG from ``numpy.random`...
TESTa25428c330sklearn.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"}}}
TESTa25428c330sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_error_score"raise-deprecating"
TESTa25428c330sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_iid"warn"
TESTa25428c330sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_iter5
TESTa25428c330sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_jobsnull
TESTa25428c330sklearn.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]}
TESTa25428c330sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TESTa25428c330sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_random_state12172
TESTa25428c330sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_refittrue
TESTa25428c330sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_return_train_scorefalse
TESTa25428c330sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_scoringnull
TESTa25428c330sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbose0
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_bootstraptrue
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_class_weightnull
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_criterion"gini"
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_max_depthnull
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_max_features"auto"
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_max_leaf_nodesnull
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_decrease0.0
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_splitnull
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_leaf1
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_split2
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_n_estimators5
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_n_jobsnull
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_oob_scorefalse
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_random_state33003
TESTa25428c330sklearn.ensemble.forest.RandomForestClassifier(1)_verbose0
TESTa25428c330sklearn.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

0.8246
Per class
Cross-validation details (10% Holdout set)
0.7472
Per class
Cross-validation details (10% Holdout set)
0.4446
Cross-validation details (10% Holdout set)
0.2855
Cross-validation details (10% Holdout set)
0.336
Cross-validation details (10% Holdout set)
0.4589
Cross-validation details (10% Holdout set)
0.7549
Cross-validation details (10% Holdout set)
253
Per class
Cross-validation details (10% Holdout set)
0.7498
Per class
Cross-validation details (10% Holdout set)
0.7549
Cross-validation details (10% Holdout set)
0.9463
Cross-validation details (10% Holdout set)
0.7322
Cross-validation details (10% Holdout set)
0.4813
Cross-validation details (10% Holdout set)
0.4025
Cross-validation details (10% Holdout set)
0.8364
Cross-validation details (10% Holdout set)
0.712
Cross-validation details (10% Holdout set)