Run
10559844

Run 10559844

Task 59 (Supervised Classification) iris Uploaded 25-03-2021 by Pieter Gijsbers
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemb le._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.
sklearn.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": "54293", "shuffle": "true"}}}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_error_scoreNaN
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_iid"deprecated"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_n_iter1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_n_jobsnull
sklearn.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]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_random_state58523
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_refittrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_return_train_scorefalse
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_scoringnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(7)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(7)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(7)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(7)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(7)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(7)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(7)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(7)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(7)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(7)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(7)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_n_estimators5
sklearn.ensemble._forest.RandomForestClassifier(7)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(7)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(7)_random_state48090
sklearn.ensemble._forest.RandomForestClassifier(7)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(7)_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
0.9728 ± 0.0349
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9466 ± 0.0536
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.92 ± 0.0789
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9286 ± 0.0627
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0359 ± 0.0302
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4444
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9467 ± 0.0526
Cross-validation details (10-fold Crossvalidation)
Number of instances
150
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9471 ± 0.0458
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9467 ± 0.0526
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.585
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0807 ± 0.068
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4714
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1707 ± 0.1077
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3622 ± 0.2285
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9467 ± 0.0526
Cross-validation details (10-fold Crossvalidation)