Run
10560202

Run 10560202

Task 9952 (Supervised Classification) phoneme Uploaded 13-08-2021 by Sergey Redyuk
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,extratreesclass ifier=sklearn.ensemble.forest.ExtraTreesClassifier)(2)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting to None.
sklearn.decomposition.pca.PCA(11)_copytrue
sklearn.decomposition.pca.PCA(11)_iterated_power"auto"
sklearn.decomposition.pca.PCA(11)_n_componentsnull
sklearn.decomposition.pca.PCA(11)_random_state51440
sklearn.decomposition.pca.PCA(11)_svd_solver"auto"
sklearn.decomposition.pca.PCA(11)_tol0.0
sklearn.decomposition.pca.PCA(11)_whitenfalse
sklearn.ensemble.forest.ExtraTreesClassifier(15)_bootstrapfalse
sklearn.ensemble.forest.ExtraTreesClassifier(15)_class_weightnull
sklearn.ensemble.forest.ExtraTreesClassifier(15)_criterion"gini"
sklearn.ensemble.forest.ExtraTreesClassifier(15)_max_depthnull
sklearn.ensemble.forest.ExtraTreesClassifier(15)_max_features"auto"
sklearn.ensemble.forest.ExtraTreesClassifier(15)_max_leaf_nodesnull
sklearn.ensemble.forest.ExtraTreesClassifier(15)_min_impurity_split1e-07
sklearn.ensemble.forest.ExtraTreesClassifier(15)_min_samples_leaf1
sklearn.ensemble.forest.ExtraTreesClassifier(15)_min_samples_split2
sklearn.ensemble.forest.ExtraTreesClassifier(15)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.ExtraTreesClassifier(15)_n_estimators100
sklearn.ensemble.forest.ExtraTreesClassifier(15)_n_jobs-1
sklearn.ensemble.forest.ExtraTreesClassifier(15)_oob_scorefalse
sklearn.ensemble.forest.ExtraTreesClassifier(15)_random_state3197
sklearn.ensemble.forest.ExtraTreesClassifier(15)_verbose0
sklearn.ensemble.forest.ExtraTreesClassifier(15)_warm_startfalse
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "pca", "step_name": "pca"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "extratreesclassifier", "step_name": "extratreesclassifier"}}]

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
0.963 ± 0.0096
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9055 ± 0.0169
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7701 ± 0.0424
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6419 ± 0.0272
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1534 ± 0.0097
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4147 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9064 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
Number of instances
5404
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9053 ± 0.0169
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9064 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8732 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.37 ± 0.0235
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4554 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.262 ± 0.0166
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5754 ± 0.0363
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8783 ± 0.0267
Cross-validation details (10-fold Crossvalidation)