Run
10568736

Run 10568736

Task 3573 (Supervised Classification) mnist_784 Uploaded 01-12-2021 by Marc Boel
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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)(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 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.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(2)_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)(2)_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)(2)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_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)(2)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_verbose_feature_names_outtrue
sklearn.impute._base.SimpleImputer(28)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(28)_copytrue
sklearn.impute._base.SimpleImputer(28)_fill_valuenull
sklearn.impute._base.SimpleImputer(28)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(28)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(28)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(30)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(30)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(30)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(30)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(30)_sparsetrue
sklearn.tree._classes.DecisionTreeClassifier(23)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(23)_criterion"entropy"
sklearn.tree._classes.DecisionTreeClassifier(23)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(23)_max_features0.2767939542516761
sklearn.tree._classes.DecisionTreeClassifier(23)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(23)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_leaf13
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_split5
sklearn.tree._classes.DecisionTreeClassifier(23)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_random_state5248
sklearn.tree._classes.DecisionTreeClassifier(23)_splitter"random"

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.

18 Evaluation measures

Area under ROC curve
0.9677 ± 0.002
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8499 ± 0.0041
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8334 ± 0.0045
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8397 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0385 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1799 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8501 ± 0.004
Cross-validation details (10-fold Crossvalidation)
Number of instances
70000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8497 ± 0.0042
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8501 ± 0.004
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3198 ± 0
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2142 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1488 ± 0.002
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.496 ± 0.0066
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8479 ± 0.004
Cross-validation details (10-fold Crossvalidation)