Run
10417336

Run 10417336

Task 75193 (Supervised Classification) covertype Uploaded 13-11-2019 by Bilge Celik
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Flow

sklearn.ensemble.forest.RandomForestClassifier(58)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(58)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(58)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(58)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(58)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(58)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(58)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(58)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(58)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(58)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(58)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(58)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(58)_n_estimators"warn"
sklearn.ensemble.forest.RandomForestClassifier(58)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(58)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(58)_random_state3312
sklearn.ensemble.forest.RandomForestClassifier(58)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(58)_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.

17 Evaluation measures

Area under ROC curve
0.9888
Per class
Cross-validation details (33% Holdout set)
F measure
0.9368
Per class
Cross-validation details (33% Holdout set)
Kappa
0.8987
Cross-validation details (33% Holdout set)
KB relative information score
0.8462
Cross-validation details (33% Holdout set)
Mean absolute error
0.0401
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.178
Cross-validation details (33% Holdout set)
Number of instances
191733
Per class
Cross-validation details (33% Holdout set)
Precision
0.9371
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.9372
Cross-validation details (33% Holdout set)
Prior entropy
1.7388
Cross-validation details (33% Holdout set)
Recall
0.9372
Per class
Cross-validation details (33% Holdout set)
Relative absolute error
0.2255
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.2983
Cross-validation details (33% Holdout set)
Root mean squared error
0.1242
Cross-validation details (33% Holdout set)
Root relative squared error
0.4162
Cross-validation details (33% Holdout set)