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2007915
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Run 2007915
Task 59 (Supervised Classification)
iris
Uploaded 06-04-2017 by
Jeroen van Hoof
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downloaded by 0 people
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Client side error: Since 'prefit=True', call transform directly
Evaluation Engine Exception: Required output files not present (e.g., arff predictions).
NumPy_1.12.0.
Python_3.6.0.
run_task
SciPy_0.19.0.
sklearn.pipeline.Pipeline
Sklearn_0.18.1.
Thu_Apr__6_18.03.50_2017
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Issue
#Downvotes for this reason
By
Flow
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,varia ncethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold ,selectfrommodel=sklearn.feature_selection.from_model.SelectFromModel(estim ator=sklearn.ensemble.forest.ExtraTreesClassifier),kneighborsclassifier=skl earn.neighbors.classification.KNeighborsClassifier)(1)
Automatically created scikit-learn flow.
sklearn.neighbors.classification.KNeighborsClassifier(7)_algorithm
auto
sklearn.neighbors.classification.KNeighborsClassifier(7)_leaf_size
30
sklearn.neighbors.classification.KNeighborsClassifier(7)_metric
minkowski
sklearn.neighbors.classification.KNeighborsClassifier(7)_metric_params
None
sklearn.neighbors.classification.KNeighborsClassifier(7)_n_jobs
-1
sklearn.neighbors.classification.KNeighborsClassifier(7)_n_neighbors
5
sklearn.neighbors.classification.KNeighborsClassifier(7)_p
2
sklearn.neighbors.classification.KNeighborsClassifier(7)_weights
distance
sklearn.ensemble.forest.ExtraTreesClassifier(5)_bootstrap
False
sklearn.ensemble.forest.ExtraTreesClassifier(5)_class_weight
None
sklearn.ensemble.forest.ExtraTreesClassifier(5)_criterion
gini
sklearn.ensemble.forest.ExtraTreesClassifier(5)_max_depth
None
sklearn.ensemble.forest.ExtraTreesClassifier(5)_max_features
auto
sklearn.ensemble.forest.ExtraTreesClassifier(5)_max_leaf_nodes
None
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_impurity_split
1e-07
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_samples_leaf
1
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_samples_split
2
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_weight_fraction_leaf
0.0
sklearn.ensemble.forest.ExtraTreesClassifier(5)_n_estimators
10
sklearn.ensemble.forest.ExtraTreesClassifier(5)_n_jobs
1
sklearn.ensemble.forest.ExtraTreesClassifier(5)_oob_score
False
sklearn.ensemble.forest.ExtraTreesClassifier(5)_random_state
None
sklearn.ensemble.forest.ExtraTreesClassifier(5)_verbose
0
sklearn.ensemble.forest.ExtraTreesClassifier(5)_warm_start
False
sklearn.feature_selection.variance_threshold.VarianceThreshold(2)_threshold
0.03
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(1)_estimator
ExtraTreesClassifier(bootstrap=False, 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, oob_score=False, random_state=None, verbose=0, warm_start=False)
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(1)_prefit
True
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(1)_threshold
None
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,selectfrommodel=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier)(1)_steps
[('dualimputer',
), ('variancethreshold', VarianceThreshold(threshold=0.03)), ('selectfrommodel', SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=False, 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, oob_score=False, random_state=None, verbose=0, warm_start=False), prefit=True, threshold=None)), ('kneighborsclassifier', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=-1, n_neighbors=5, p=2, weights='distance'))]
Result files
xml
Description
XML file describing the run, including user-defined evaluation measures.
0 Evaluation measures