Run
10591863

Run 10591863

Task 3954 (Supervised Classification) MagicTelescope Uploaded 20-02-2023 by Saurav Das
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.ensemble._forest.RandomForestClassifier(23)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(23)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(23)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(23)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(23)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(23)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(23)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(23)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(23)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(23)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(23)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(23)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(23)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(23)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(23)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(23)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(23)_random_state58241
sklearn.ensemble._forest.RandomForestClassifier(23)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(23)_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
0.9361 ± 0.0063
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8786 ± 0.0071
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7306 ± 0.016
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5968 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1945 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.456 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8806 ± 0.0067
Cross-validation details (10-fold Crossvalidation)
Number of instances
19020
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8803 ± 0.0068
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8806 ± 0.0067
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9355 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4265 ± 0.0085
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4775 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2996 ± 0.0061
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6275 ± 0.0127
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8551 ± 0.009
Cross-validation details (10-fold Crossvalidation)