Run
10595177

Run 10595177

Task 3954 (Supervised Classification) MagicTelescope Uploaded 02-12-2024 by Francisco Pinto
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Flow

sklearn.ensemble._forest.RandomForestClassifier(48)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(48)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(48)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(48)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(48)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(48)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(48)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(48)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(48)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(48)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(48)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(48)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(48)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(48)_monotonic_cstnull
sklearn.ensemble._forest.RandomForestClassifier(48)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(48)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(48)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(48)_random_state4267
sklearn.ensemble._forest.RandomForestClassifier(48)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(48)_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
0.9367 ± 0.0056
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8793 ± 0.007
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7322 ± 0.0159
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5967 ± 0.0092
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1946 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.456 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8813 ± 0.0066
Cross-validation details (10-fold Crossvalidation)
Number of instances
19020
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.881 ± 0.0065
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8813 ± 0.0066
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9355 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4267 ± 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
0.2992 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6266 ± 0.0116
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8559 ± 0.0094
Cross-validation details (10-fold Crossvalidation)