Run
264

Run 264

Task 11 (Supervised Classification) kr-vs-kp Uploaded 29-10-2019 by Continuous Integration
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python Sklearn_0.19.2. study_35 study_52 study_81
Issue #Downvotes for this reason By


Flow

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemb le.forest.RandomForestClassifier)(3)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.
sklearn.ensemble.forest.RandomForestClassifier(3)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(3)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(3)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(3)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(3)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(3)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(3)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(3)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(3)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(3)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(3)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(3)_n_estimators5
sklearn.ensemble.forest.RandomForestClassifier(3)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(3)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(3)_random_state33003
sklearn.ensemble.forest.RandomForestClassifier(3)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(3)_warm_startfalse
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_cv{"oml-python:serialized_object": "cv_object", "value": {"name": "sklearn.model_selection._split.StratifiedKFold", "parameters": {"n_splits": "2", "random_state": "62501", "shuffle": "true"}}}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_error_score"raise"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_fit_paramsnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_iidtrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_n_iter2
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_n_jobs1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_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]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_random_state12172
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_refittrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_return_train_score"warn"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_scoringnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(3)_verbose0

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

0.9369
Per class
Cross-validation details (10% Holdout set)
0.853
Per class
Cross-validation details (10% Holdout set)
0.7048
Cross-validation details (10% Holdout set)
0.431
Cross-validation details (10% Holdout set)
0.3065
Cross-validation details (10% Holdout set)
0.4984
Cross-validation details (10% Holdout set)
0.8529
Cross-validation details (10% Holdout set)
1054
Per class
Cross-validation details (10% Holdout set)
0.8532
Per class
Cross-validation details (10% Holdout set)
0.8529
Cross-validation details (10% Holdout set)
0.9969
Cross-validation details (10% Holdout set)
0.6149
Cross-validation details (10% Holdout set)
0.4989
Cross-validation details (10% Holdout set)
0.3513
Cross-validation details (10% Holdout set)
0.7042
Cross-validation details (10% Holdout set)
0.8528
Cross-validation details (10% Holdout set)