Run
10559855

Run 10559855

Task 4794 (Supervised Regression) cristalli Uploaded 26-03-2021 by Tan Zheng
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Flow

sklearn.ensemble._forest.RandomForestRegressor(1)A random forest regressor. A random forest is a meta estimator that fits a number of classifying decision trees 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.RandomForestRegressor(1)_bootstraptrue
sklearn.ensemble._forest.RandomForestRegressor(1)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestRegressor(1)_criterion"mse"
sklearn.ensemble._forest.RandomForestRegressor(1)_max_depthnull
sklearn.ensemble._forest.RandomForestRegressor(1)_max_features"auto"
sklearn.ensemble._forest.RandomForestRegressor(1)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestRegressor(1)_max_samplesnull
sklearn.ensemble._forest.RandomForestRegressor(1)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestRegressor(1)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestRegressor(1)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestRegressor(1)_min_samples_split2
sklearn.ensemble._forest.RandomForestRegressor(1)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestRegressor(1)_n_estimators20
sklearn.ensemble._forest.RandomForestRegressor(1)_n_jobsnull
sklearn.ensemble._forest.RandomForestRegressor(1)_oob_scorefalse
sklearn.ensemble._forest.RandomForestRegressor(1)_random_state872
sklearn.ensemble._forest.RandomForestRegressor(1)_verbose0
sklearn.ensemble._forest.RandomForestRegressor(1)_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 prior absolute error
0.1809 ± 0.0912
Cross-validation details (10-fold Crossvalidation)
Number of instances
32
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2705 ± 0.149
Cross-validation details (10-fold Crossvalidation)