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10594103

Run 10594103

Task 3954 (Supervised Classification) MagicTelescope Uploaded 28-01-2024 by Donald Wynn
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Flow

sklearn.ensemble._forest.RandomForestClassifier(34)	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. 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(34)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(34)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(34)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(34)_max_depth	null
sklearn.ensemble._forest.RandomForestClassifier(34)_max_features	"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(34)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(34)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(34)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(34)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(34)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_n_estimators	100
sklearn.ensemble._forest.RandomForestClassifier(34)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(34)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(34)_random_state	54889
sklearn.ensemble._forest.RandomForestClassifier(34)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(34)_warm_start	false

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

0.9366 ± 0.0058

Per class

Cross-validation details (10-fold Crossvalidation)

0.8785 ± 0.0068

Per class

Cross-validation details (10-fold Crossvalidation)

0.7302 ± 0.0154

Cross-validation details (10-fold Crossvalidation)

KB relative information score

0.5973 ± 0.0096

Cross-validation details (10-fold Crossvalidation)

Mean absolute error

0.1944 ± 0.0038

Cross-validation details (10-fold Crossvalidation)

Mean prior absolute error

0.456 ± 0.0001

Cross-validation details (10-fold Crossvalidation)

Weighted recall

0.8804 ± 0.0065

Cross-validation details (10-fold Crossvalidation)

Number of instances

19020

Per class

Cross-validation details (10-fold Crossvalidation)

0.8802 ± 0.0066

Per class

Cross-validation details (10-fold Crossvalidation)

Predictive accuracy

0.8804 ± 0.0065

Cross-validation details (10-fold Crossvalidation)

Prior entropy

0.9355 ± 0.0002

Cross-validation details (10-fold Crossvalidation)

Relative absolute error

0.4263 ± 0.0084

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

0.2989 ± 0.006

Cross-validation details (10-fold Crossvalidation)

Root relative squared error

0.6261 ± 0.0125

Cross-validation details (10-fold Crossvalidation)

Unweighted recall

0.8549 ± 0.0087

Cross-validation details (10-fold Crossvalidation)