Run
10456881

Run 10456881

Task 211993 (Supervised Regression) titanic_2 Uploaded 19-05-2020 by sri ranga sai
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  • openml-python Sklearn_0.22.2.post1.
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Flow

sklearn.ensemble._forest.RandomForestClassifier(3)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(3)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(3)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(3)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(3)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(3)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(3)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(3)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(3)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(3)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(3)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(3)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(3)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(3)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(3)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(3)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(3)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(3)_random_state28567
sklearn.ensemble._forest.RandomForestClassifier(3)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(3)_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.

7 Evaluation measures

Mean absolute error
0.1874 ± 0.0445
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.473 ± 0.0124
Cross-validation details (10-fold Crossvalidation)
Number of instances
891
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3962 ± 0.0995
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4863 ± 0.0129
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4329 ± 0.0523
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8902 ± 0.1175
Cross-validation details (10-fold Crossvalidation)