Run
7951671

Run 7951671

Task 119 (Learning Curve) zoo Uploaded 16-10-2017 by Toon Van Craenendonck
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Flow

TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator= sklearn.ensemble.forest.RandomForestClassifier)(1)Automatically created scikit-learn flow.
TESTaca66164efsklearn.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"}}}
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_error_score"raise"
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_fit_params{}
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_iidtrue
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_iter5
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_jobs1
TESTaca66164efsklearn.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]}
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_random_state12172
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_refittrue
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_return_train_scoretrue
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_scoringnull
TESTaca66164efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbose0
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_bootstraptrue
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_class_weightnull
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_criterion"gini"
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_max_depthnull
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_max_features"auto"
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_max_leaf_nodesnull
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_split1e-07
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_leaf1
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_split2
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_n_estimators5
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_n_jobs1
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_oob_scorefalse
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_random_state33003
TESTaca66164efsklearn.ensemble.forest.RandomForestClassifier(1)_verbose0
TESTaca66164efsklearn.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.

17 Evaluation measures

Area under ROC curve
0.9937 ± 0.0123
Per class
F measure
0.9159 ± 0.0251
Per class
Kappa
0.8952 ± 0.1064
KB relative information score
830.3108 ± 0.8511
Mean absolute error
0.0497 ± 0.0229
Mean prior absolute error
0.2188 ± 0.0049
Number of instances
1010
Per class
Precision
0.9171 ± 0.0156
Per class
Predictive accuracy
0.9208 ± 0.0794
Prior entropy
2.4427
Recall
0.9208 ± 0.0794
Per class
Relative absolute error
0.2273 ± 0.1033
Root mean prior squared error
0.3294 ± 0.0075
Root mean squared error
0.1339 ± 0.0425
Root relative squared error
0.4064 ± 0.1265