Run
9263

Run 9263

Task 119 (Supervised Classification) diabetes Uploaded 25-11-2022 by Continuous Integration
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Flow

TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklear n.ensemble.forest.RandomForestClassifier)(1)Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV 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 grid-search over a parameter grid.
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(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"}}}
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_error_score"raise"
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_fit_paramsnull
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_iidtrue
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_jobs1
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_param_grid[{"max_features": [2, 4]}, {"min_samples_leaf": [1, 10]}]
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_refittrue
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_return_train_score"warn"
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_scoringnull
TESTdb34b682fdsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbose0
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_bootstraptrue
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_class_weightnull
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_criterion"gini"
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_max_depthnull
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_max_features"auto"
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_max_leaf_nodesnull
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_decrease0.0
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_splitnull
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_leaf1
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_split2
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_n_estimators5
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_n_jobs1
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_oob_scorefalse
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_random_state33003
TESTdb34b682fdsklearn.ensemble.forest.RandomForestClassifier(1)_verbose0
TESTdb34b682fdsklearn.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
0.8066
Per class
F measure
0.7544
Per class
Kappa
0.4628
KB relative information score
0.2671
Mean absolute error
0.34
Mean prior absolute error
0.4589
Weighted recall
0.7589
Number of instances
253
Per class
Precision
0.7542
Per class
Predictive accuracy
0.7589
Prior entropy
0.9463
Relative absolute error
0.7408
Root mean prior squared error
0.4813
Root mean squared error
0.4121
Root relative squared error
0.8562
Unweighted recall
0.7244