Run
10593924

Run 10593924

Task 31 (Supervised Classification) credit-g Uploaded 17-07-2023 by ck kc
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,one hotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclass ifier=sklearn.tree._classes.DecisionTreeClassifier)(7)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 transformers in the pipeline can be cached using ``memory`` argument. 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 it to `'passthrough'` or `None`.
sklearn.preprocessing._encoders.OneHotEncoder(44)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(44)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(44)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(44)_feature_name_combiner"concat"
sklearn.preprocessing._encoders.OneHotEncoder(44)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(44)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(44)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(44)_sparse"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(44)_sparse_outputtrue
sklearn.impute._base.SimpleImputer(48)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(48)_copytrue
sklearn.impute._base.SimpleImputer(48)_fill_valuenull
sklearn.impute._base.SimpleImputer(48)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(48)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(48)_strategy"mean"
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(7)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(7)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(7)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}]
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_verbose_feature_names_outtrue
sklearn.tree._classes.DecisionTreeClassifier(40)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(40)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(40)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(40)_max_depth1
sklearn.tree._classes.DecisionTreeClassifier(40)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(40)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(40)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(40)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(40)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(40)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(40)_random_state55659
sklearn.tree._classes.DecisionTreeClassifier(40)_splitter"best"

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

Area under ROC curve
0.6506 ± 0.0396
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.061 ± 0.0438
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3766 ± 0.0139
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8813
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8963 ± 0.0332
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4341 ± 0.0109
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9472 ± 0.0238
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5
Cross-validation details (10-fold Crossvalidation)