Run
1949235

Run 1949235

Task 49 (Supervised Classification) tic-tac-toe Uploaded 02-04-2017 by Jeroen van Hoof
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • NumPy_1.12.1. Python_3.5.3. run_task SciPy_0.18.1. sklearn.pipeline.Pipeline Sklearn_0.18.1. Sun_Apr__2_21.56.16_2017
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,stand ardscaler=sklearn.preprocessing.data.StandardScaler,kneighborsclassifier=sk learn.neighbors.classification.KNeighborsClassifier)(1)Automatically created scikit-learn flow.
sklearn.neighbors.classification.KNeighborsClassifier(7)_algorithmauto
sklearn.neighbors.classification.KNeighborsClassifier(7)_leaf_size30
sklearn.neighbors.classification.KNeighborsClassifier(7)_metricminkowski
sklearn.neighbors.classification.KNeighborsClassifier(7)_metric_paramsNone
sklearn.neighbors.classification.KNeighborsClassifier(7)_n_jobs-1
sklearn.neighbors.classification.KNeighborsClassifier(7)_n_neighbors5
sklearn.neighbors.classification.KNeighborsClassifier(7)_p2
sklearn.neighbors.classification.KNeighborsClassifier(7)_weightsuniform
sklearn.preprocessing.data.StandardScaler(1)_copyTrue
sklearn.preprocessing.data.StandardScaler(1)_with_meanTrue
sklearn.preprocessing.data.StandardScaler(1)_with_stdTrue
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier)(1)_steps[('dualimputer', ), ('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('kneighborsclassifier', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=-1, n_neighbors=5, p=2, weights='uniform'))]

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
0.8188
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8448
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.649
Cross-validation details (10-fold Crossvalidation)
KB relative information score
526.316
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2098
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.453
Cross-validation details (10-fold Crossvalidation)
Number of instances
958
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8672
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8539
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9312
Cross-validation details (10-fold Crossvalidation)
Recall
0.8539
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4632
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4759
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3663
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7697
Cross-validation details (10-fold Crossvalidation)