Run
10587873

Run 10587873

Task 361001 (Supervised Classification) iris Uploaded 20-06-2022 by Iris Walter
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Flow

sklearn.ensemble._forest.RandomForestClassifier(12)A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the `max_samples` parameter if `bootstrap=True` (default), otherwise the whole dataset is used to build each tree.
sklearn.ensemble._forest.RandomForestClassifier(12)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(12)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(12)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(12)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(12)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(12)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(12)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(12)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(12)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(12)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(12)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(12)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(12)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(12)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(12)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(12)_random_state42359
sklearn.ensemble._forest.RandomForestClassifier(12)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(12)_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.

18 Evaluation measures

Area under ROC curve
0.9911
Per class
Cross-validation details (33% Holdout set)
F measure
0.9386
Per class
Cross-validation details (33% Holdout set)
Kappa
0.9082
Cross-validation details (33% Holdout set)
KB relative information score
0.9012
Cross-validation details (33% Holdout set)
Mean absolute error
0.0535
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.4444
Cross-validation details (33% Holdout set)
Weighted recall
0.9388
Cross-validation details (33% Holdout set)
Number of instances
49
Per class
Cross-validation details (33% Holdout set)
Precision
0.9484
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.9388
Cross-validation details (33% Holdout set)
Prior entropy
1.585
Cross-validation details (33% Holdout set)
Relative absolute error
0.1203
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.4714
Cross-validation details (33% Holdout set)
Root mean squared error
0.1716
Cross-validation details (33% Holdout set)
Root relative squared error
0.3639
Cross-validation details (33% Holdout set)
Unweighted recall
0.9444
Cross-validation details (33% Holdout set)