Run
10595156

Run 10595156

Task 3954 (Supervised Classification) MagicTelescope Uploaded 19-09-2024 by Maurice Melotto
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.ensemble._forest.RandomForestClassifier(46)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. Trees in the forest use the best split strategy, i.e. equivalent to passing `splitter="best"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`. 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. For a comparison between tree-based ensemble models see the example :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.
sklearn.ensemble._forest.RandomForestClassifier(46)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(46)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(46)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(46)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(46)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(46)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(46)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(46)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(46)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(46)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(46)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(46)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(46)_monotonic_cstnull
sklearn.ensemble._forest.RandomForestClassifier(46)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(46)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(46)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(46)_random_state37678
sklearn.ensemble._forest.RandomForestClassifier(46)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(46)_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
0.9365 ± 0.0059
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8786 ± 0.0071
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7305 ± 0.0162
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.597 ± 0.0092
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1944 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.456 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8805 ± 0.0067
Cross-validation details (10-fold Crossvalidation)
Number of instances
19020
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8803 ± 0.0067
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8805 ± 0.0067
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9355 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4263 ± 0.0081
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
0.2993 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6268 ± 0.0121
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8551 ± 0.0097
Cross-validation details (10-fold Crossvalidation)