Run
10434612

Run 10434612

Task 211237 (Supervised Regression) dataset-autoHorse_fixed Uploaded 19-12-2019 by George Volkov
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Flow

sklearn.ensemble.forest.RandomForestRegressor(2)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 always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default).
sklearn.ensemble.forest.RandomForestRegressor(2)_bootstraptrue
sklearn.ensemble.forest.RandomForestRegressor(2)_criterion"mse"
sklearn.ensemble.forest.RandomForestRegressor(2)_max_depthnull
sklearn.ensemble.forest.RandomForestRegressor(2)_max_features"auto"
sklearn.ensemble.forest.RandomForestRegressor(2)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestRegressor(2)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestRegressor(2)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestRegressor(2)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestRegressor(2)_min_samples_split2
sklearn.ensemble.forest.RandomForestRegressor(2)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestRegressor(2)_n_estimators1000
sklearn.ensemble.forest.RandomForestRegressor(2)_n_jobsnull
sklearn.ensemble.forest.RandomForestRegressor(2)_oob_scorefalse
sklearn.ensemble.forest.RandomForestRegressor(2)_random_state47769
sklearn.ensemble.forest.RandomForestRegressor(2)_verbose0
sklearn.ensemble.forest.RandomForestRegressor(2)_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
1385.3544 ± 325.9881
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
5841.1883 ± 1722.8879
Cross-validation details (10-fold Crossvalidation)
Number of instances
201
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2372 ± 0.0894
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
7927.2729 ± 2557.8253
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
2094.1428 ± 565.0876
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.2642 ± 0.0834
Cross-validation details (10-fold Crossvalidation)