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1852499

Run 1852499

Task 145677 (Supervised Classification) Bioresponse Uploaded 13-03-2017 by Xiaolei Wang
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  • Mon_Mar_13_21.33.09_2017 NumPy_1.11.3. Python_3.5.2. run_task SciPy_0.18.1. sklearn.model_selection._search.GridSearchCV Sklearn_0.18.1.
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pip eline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.featu re_selection.univariate_selection.SelectPercentile,scaler=sklearn.preproces sing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighbo rsClassifier))(1)Automatically created sub-component.
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_jobs1
sklearn.neighbors.classification.KNeighborsClassifier(7)_n_neighbors5
sklearn.neighbors.classification.KNeighborsClassifier(7)_p2
sklearn.neighbors.classification.KNeighborsClassifier(7)_weightsuniform
sklearn.preprocessing.imputation.Imputer(3)_axis0
sklearn.preprocessing.imputation.Imputer(3)_copyTrue
sklearn.preprocessing.imputation.Imputer(3)_missing_valuesNaN
sklearn.preprocessing.imputation.Imputer(3)_strategymean
sklearn.preprocessing.imputation.Imputer(3)_verbose0
sklearn.feature_selection.univariate_selection.SelectPercentile(1)_percentile5
sklearn.feature_selection.univariate_selection.SelectPercentile(1)_score_func
sklearn.preprocessing.data.MinMaxScaler(1)_copyTrue
sklearn.preprocessing.data.MinMaxScaler(1)_feature_range(0, 1)
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_cv10
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_error_scoreraise
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_estimatorPipeline(steps=[('Imputer', Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)), ('select', SelectPercentile(percentile=5, score_func=)), ('scaler', MinMaxScaler(copy=True, feature_range=(0, 1))), ('Classifier', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2, weights='uniform'))])
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_fit_params{}
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_iidTrue
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_n_jobs-1
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_param_grid{'Classifier__n_neighbors': [1, 3, 5, 7, 10]}
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_pre_dispatch2*n_jobs
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_refitTrue
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_return_train_scoreTrue
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_scoringroc_auc
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier))(1)_verbose0
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier)(1)_steps[('Imputer', Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)), ('select', SelectPercentile(percentile=5, score_func=)), ('scaler', MinMaxScaler(copy=True, feature_range=(0, 1))), ('Classifier', 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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

17 Evaluation measures

0.8195
Per class
Cross-validation details (10-fold Crossvalidation)
0.7465
Per class
Cross-validation details (10-fold Crossvalidation)
0.4893
Cross-validation details (10-fold Crossvalidation)
1429.6714
Cross-validation details (10-fold Crossvalidation)
0.317
Cross-validation details (10-fold Crossvalidation)
0.4964
Cross-validation details (10-fold Crossvalidation)
3751
Per class
Cross-validation details (10-fold Crossvalidation)
0.7465
Per class
Cross-validation details (10-fold Crossvalidation)
0.7465
Cross-validation details (10-fold Crossvalidation)
0.9948
Cross-validation details (10-fold Crossvalidation)
0.7465
Per class
Cross-validation details (10-fold Crossvalidation)
0.6385
Cross-validation details (10-fold Crossvalidation)
0.4982
Cross-validation details (10-fold Crossvalidation)
0.4175
Cross-validation details (10-fold Crossvalidation)
0.8379
Cross-validation details (10-fold Crossvalidation)