Run
8885545

Run 8885545

Task 167132 (Supervised Classification) Speech Uploaded 14-03-2018 by Nick Braat
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Flow

sklearn.pipeline.Pipeline(feature_selection=sklearn.feature_selection.univa riate_selection.SelectKBest,random_forest=sklearn.ensemble.forest.RandomFor estClassifier)(1)Automatically created scikit-learn flow.
sklearn.ensemble.forest.RandomForestClassifier(32)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(32)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(32)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(32)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(32)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(32)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(32)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(32)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(32)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(32)_random_state20064
sklearn.ensemble.forest.RandomForestClassifier(32)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(32)_warm_startfalse
sklearn.feature_selection.univariate_selection.SelectKBest(2)_k100
sklearn.feature_selection.univariate_selection.SelectKBest(2)_score_func{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection.univariate_selection.f_classif"}
sklearn.pipeline.Pipeline(feature_selection=sklearn.feature_selection.univariate_selection.SelectKBest,random_forest=sklearn.ensemble.forest.RandomForestClassifier)(1)_memorynull

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.

15 Evaluation measures

Area under ROC curve
0.5775 ± 0.0989
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-12123.6429 ± 99.7744
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0355 ± 0.0026
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.0328 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Number of instances
3686
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9835 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.1231
Cross-validation details (10-fold Crossvalidation)
Recall
0.9835 ± 0.0009
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0809 ± 0.0725
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.1276 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1351 ± 0.0048
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0586 ± 0.0327
Cross-validation details (10-fold Crossvalidation)