Run
1852404

Run 1852404

Task 145677 (Supervised Classification) Bioresponse Uploaded 13-03-2017 by Jeroen van Hoof
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  • Mon_Mar_13_13.59.19_2017 NumPy_1.11.2. Python_3.5.2. run_task SciPy_0.18.1. sklearn.pipeline.Pipeline Sklearn_0.18.1.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sk learn.feature_selection.variance_threshold.VarianceThreshold,randomforestcl assifier=sklearn.ensemble.forest.RandomForestClassifier)(1)Automatically created scikit-learn flow.
sklearn.ensemble.forest.RandomForestClassifier(16)_bootstrapTrue
sklearn.ensemble.forest.RandomForestClassifier(16)_class_weightNone
sklearn.ensemble.forest.RandomForestClassifier(16)_criteriongini
sklearn.ensemble.forest.RandomForestClassifier(16)_max_depthNone
sklearn.ensemble.forest.RandomForestClassifier(16)_max_featuresauto
sklearn.ensemble.forest.RandomForestClassifier(16)_max_leaf_nodesNone
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(16)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(16)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(16)_oob_scoreFalse
sklearn.ensemble.forest.RandomForestClassifier(16)_random_stateNone
sklearn.ensemble.forest.RandomForestClassifier(16)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(16)_warm_startFalse
sklearn.preprocessing.imputation.Imputer(3)_axis0
sklearn.preprocessing.imputation.Imputer(3)_copyTrue
sklearn.preprocessing.imputation.Imputer(3)_missing_valuesNaN
sklearn.preprocessing.imputation.Imputer(3)_strategymedian
sklearn.preprocessing.imputation.Imputer(3)_verbose0
sklearn.preprocessing.data.OneHotEncoder(3)_categorical_features[False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, F
sklearn.preprocessing.data.OneHotEncoder(3)_dtype
sklearn.preprocessing.data.OneHotEncoder(3)_handle_unknownignore
sklearn.preprocessing.data.OneHotEncoder(3)_n_valuesauto
sklearn.preprocessing.data.OneHotEncoder(3)_sparseFalse
sklearn.feature_selection.variance_threshold.VarianceThreshold(2)_threshold0.0
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps[('imputer', Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)), ('onehotencoder', OneHotEncoder(categorical_features=[False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False...e, False, False, False, False, False, False, False, False, False, False, False, False, False, False], dtype=, handle_unknown='ignore', n_values='auto', sparse=False)), ('variancethreshold', VarianceThreshold(threshold=0.0)), ('randomforestclassifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,

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
0.8447
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7751
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.5482
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1474.9288
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3148
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4964
Cross-validation details (10-fold Crossvalidation)
Number of instances
3751
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7765
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7747
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9948
Cross-validation details (10-fold Crossvalidation)
Recall
0.7747
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6342
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4982
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3995
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8019
Cross-validation details (10-fold Crossvalidation)