Run
10587866

Run 10587866

Task 119 (Learning Curve) zoo Uploaded 15-06-2022 by Tan Tran
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


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_state23307
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
0.999 ± 0.0044
Per class
F measure
0.9607
Per class
Kappa
0.9516 ± 0.078
KB relative information score
0.9251 ± 0.0599
Mean absolute error
0.0234 ± 0.0137
Mean prior absolute error
0.2188 ± 0.0049
Weighted recall
0.9634 ± 0.059
Number of instances
1010
Per class
Precision
0.9625
Per class
Predictive accuracy
0.9634 ± 0.059
Prior entropy
2.3926 ± 0.137
Relative absolute error
0.1071 ± 0.0616
Root mean prior squared error
0.3294 ± 0.0075
Root mean squared error
0.0859 ± 0.0417
Root relative squared error
0.2607 ± 0.1246
Unweighted recall
0.905 ± 0.101