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Run 9071733
Task 146817 (Supervised Classification)
steel-plates-fault
Uploaded 14-04-2018 by
Hilde Weerts
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sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.Condition alImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethr eshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=s klearn.ensemble.forest.RandomForestClassifier)(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.ensemble.forest.RandomForestClassifier(32)_bootstrap
true
sklearn.ensemble.forest.RandomForestClassifier(32)_class_weight
null
sklearn.ensemble.forest.RandomForestClassifier(32)_criterion
"gini"
sklearn.ensemble.forest.RandomForestClassifier(32)_max_depth
null
sklearn.ensemble.forest.RandomForestClassifier(32)_max_features
0.9762123824405095
sklearn.ensemble.forest.RandomForestClassifier(32)_max_leaf_nodes
null
sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_decrease
0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_split
null
sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_leaf
2
sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_split
16
sklearn.ensemble.forest.RandomForestClassifier(32)_min_weight_fraction_leaf
0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_n_estimators
300
sklearn.ensemble.forest.RandomForestClassifier(32)_n_jobs
1
sklearn.ensemble.forest.RandomForestClassifier(32)_oob_score
false
sklearn.ensemble.forest.RandomForestClassifier(32)_random_state
30836
sklearn.ensemble.forest.RandomForestClassifier(32)_verbose
0
sklearn.ensemble.forest.RandomForestClassifier(32)_warm_start
false
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier)(1)_memory
null
hyperimp.utils.preprocessing.ConditionalImputer2(1)_axis
0
hyperimp.utils.preprocessing.ConditionalImputer2(1)_categorical_features
[]
hyperimp.utils.preprocessing.ConditionalImputer2(1)_copy
true
hyperimp.utils.preprocessing.ConditionalImputer2(1)_fill_empty
0
hyperimp.utils.preprocessing.ConditionalImputer2(1)_missing_values
"NaN"
hyperimp.utils.preprocessing.ConditionalImputer2(1)_strategy
"mean"
hyperimp.utils.preprocessing.ConditionalImputer2(1)_strategy_nominal
"most_frequent"
hyperimp.utils.preprocessing.ConditionalImputer2(1)_verbose
0
Result files
0 Evaluation measures