Run
10452857

Run 10452857

Task 119 (Learning Curve) zoo Uploaded 16-05-2020 by Sanath Shenoy
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Flow

sklearn.ensemble._forest.RandomForestClassifier(2)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 always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default).
sklearn.ensemble._forest.RandomForestClassifier(2)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state45741
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_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
0.9991 ± 0.0041
Per class
F measure
0.956
Per class
Kappa
0.9463 ± 0.0821
KB relative information score
0.924 ± 0.0578
Mean absolute error
0.024 ± 0.0136
Mean prior absolute error
0.2188 ± 0.0049
Weighted recall
0.9594 ± 0.0622
Number of instances
1010
Per class
Precision
0.9575
Per class
Predictive accuracy
0.9594 ± 0.0622
Prior entropy
2.3926 ± 0.137
Relative absolute error
0.1099 ± 0.0609
Root mean prior squared error
0.3294 ± 0.0075
Root mean squared error
0.087 ± 0.0413
Root relative squared error
0.2641 ± 0.1231
Unweighted recall
0.8929 ± 0.1313