Run
10595117

Run 10595117

Task 37 (Supervised Classification) diabetes Uploaded 09-07-2024 by Kedar Nath Chhatkuli
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.ensemble._forest.RandomForestClassifier(45)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 controlled with the `max_samples` parameter if `bootstrap=True` (default), otherwise the whole dataset is used to build each tree.
sklearn.ensemble._forest.RandomForestClassifier(45)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(45)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(45)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(45)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(45)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(45)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(45)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(45)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(45)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(45)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(45)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(45)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(45)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(45)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(45)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(45)_random_state57659
sklearn.ensemble._forest.RandomForestClassifier(45)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(45)_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.

18 Evaluation measures

Area under ROC curve
0.8252 ± 0.0413
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7654 ± 0.0477
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4757 ± 0.1078
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3071 ± 0.0622
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3196 ± 0.0232
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7708 ± 0.0458
Cross-validation details (10-fold Crossvalidation)
Number of instances
768
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7655 ± 0.0511
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7708 ± 0.0458
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7033 ± 0.0514
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4006 ± 0.0259
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8404 ± 0.055
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7288 ± 0.0527
Cross-validation details (10-fold Crossvalidation)