Run
10560457

Run 10560457

Task 43 (Supervised Classification) spambase Uploaded 14-08-2021 by Sergey Redyuk
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imput er,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=s klearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sk learn.tree.tree.DecisionTreeClassifier)(2)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting to None.
sklearn.preprocessing.imputation.Imputer(52)_axis0
sklearn.preprocessing.imputation.Imputer(52)_copytrue
sklearn.preprocessing.imputation.Imputer(52)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(52)_strategy"median"
sklearn.preprocessing.imputation.Imputer(52)_verbose0
sklearn.tree.tree.DecisionTreeClassifier(66)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(66)_criterion"entropy"
sklearn.tree.tree.DecisionTreeClassifier(66)_max_depth0.9666063820857091
sklearn.tree.tree.DecisionTreeClassifier(66)_max_features1.0
sklearn.tree.tree.DecisionTreeClassifier(66)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(66)_min_impurity_split1e-07
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_leaf3
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_split9
sklearn.tree.tree.DecisionTreeClassifier(66)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(66)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(66)_random_state44146
sklearn.tree.tree.DecisionTreeClassifier(66)_splitter"best"
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.tree.tree.DecisionTreeClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputation", "step_name": "imputation"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "hotencoding", "step_name": "hotencoding"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variencethreshold", "step_name": "variencethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.preprocessing.data.OneHotEncoder(30)_categorical_features[3, 4, 7, 8]
sklearn.preprocessing.data.OneHotEncoder(30)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(30)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(30)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(30)_sparsefalse
sklearn.feature_selection.variance_threshold.VarianceThreshold(30)_threshold0.0

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.

16 Evaluation measures

0.4991
Per class
Cross-validation details (10-fold Crossvalidation)
0 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.4775 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.4776 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.606 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
4601
Per class
Cross-validation details (10-fold Crossvalidation)
0.606 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
0.9674 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
1 ± 0
Cross-validation details (10-fold Crossvalidation)
0.4886 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.4886 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
1 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)