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Run 161

Task 115 (Supervised Classification) diabetes Uploaded 11-01-2024 by Continuous Integration
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sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Varian ceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold ,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth4
sklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
sklearn.tree._classes.DecisionTreeClassifier(1)_splitter"best"
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "VarianceThreshold", "step_name": "VarianceThreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Estimator", "step_name": "Estimator"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbosefalse
sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(1)_copytrue
sklearn.impute._base.SimpleImputer(1)_fill_valuenull
sklearn.impute._base.SimpleImputer(1)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(1)_strategy"most_frequent"
sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold0.1

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

0.7953 ± 0.0406
Per class
0.7324 ± 0.0452
Per class
0.4127 ± 0.0932
0.3128 ± 0.0642
0.3112 ± 0.0269
0.4545 ± 0.0011
0.7318 ± 0.0408
768
Per class
0.7332 ± 0.0353
Per class
0.7318 ± 0.0408
0.9331 ± 0.0032
0.6848 ± 0.0593
0.4766 ± 0.0011
0.4203 ± 0.0199
0.8817 ± 0.0422
0.7074 ± 0.0501