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Run 9026468
Task 146817 (Supervised Classification)
steel-plates-fault
Uploaded 10-04-2018 by
Hilde Weerts
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sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.Condition alImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=skle arn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_sel ection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1)
Automatically created scikit-learn flow.
sklearn.preprocessing.data.OneHotEncoder(17)_categorical_features
[]
sklearn.preprocessing.data.OneHotEncoder(17)_dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(17)_handle_unknown
"ignore"
sklearn.preprocessing.data.OneHotEncoder(17)_n_values
"auto"
sklearn.preprocessing.data.OneHotEncoder(17)_sparse
true
sklearn.feature_selection.variance_threshold.VarianceThreshold(11)_threshold
0.0
sklearn.preprocessing.data.StandardScaler(5)_copy
true
sklearn.preprocessing.data.StandardScaler(5)_with_mean
false
sklearn.preprocessing.data.StandardScaler(5)_with_std
true
sklearn.svm.classes.SVC(16)_C
3.721312169066857
sklearn.svm.classes.SVC(16)_cache_size
200
sklearn.svm.classes.SVC(16)_class_weight
null
sklearn.svm.classes.SVC(16)_coef0
0.3447905709324064
sklearn.svm.classes.SVC(16)_decision_function_shape
"ovr"
sklearn.svm.classes.SVC(16)_degree
3
sklearn.svm.classes.SVC(16)_gamma
0.002253261199551664
sklearn.svm.classes.SVC(16)_kernel
"rbf"
sklearn.svm.classes.SVC(16)_max_iter
-1
sklearn.svm.classes.SVC(16)_probability
false
sklearn.svm.classes.SVC(16)_random_state
1
sklearn.svm.classes.SVC(16)_shrinking
true
sklearn.svm.classes.SVC(16)_tol
0.0002394210238833234
sklearn.svm.classes.SVC(16)_verbose
false
hyperimp.utils.preprocessing.ConditionalImputer(1)_axis
0
hyperimp.utils.preprocessing.ConditionalImputer(1)_categorical_features
[]
hyperimp.utils.preprocessing.ConditionalImputer(1)_copy
true
hyperimp.utils.preprocessing.ConditionalImputer(1)_fill_empty
0
hyperimp.utils.preprocessing.ConditionalImputer(1)_missing_values
"NaN"
hyperimp.utils.preprocessing.ConditionalImputer(1)_strategy
"mean"
hyperimp.utils.preprocessing.ConditionalImputer(1)_strategy_nominal
"most_frequent"
hyperimp.utils.preprocessing.ConditionalImputer(1)_verbose
0
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1)_memory
null
Result files
0 Evaluation measures