Run
10595174

Run 10595174

Task 52950 (Supervised Classification) higgs Uploaded 30-11-2024 by José Evans
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,knn=sk learn.neighbors._classification.KNeighborsClassifier)(1)A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`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:`~s...
sklearn.impute._base.SimpleImputer(58)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(58)_copytrue
sklearn.impute._base.SimpleImputer(58)_fill_valuenull
sklearn.impute._base.SimpleImputer(58)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(58)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(58)_strategy"mean"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,knn=sklearn.neighbors._classification.KNeighborsClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,knn=sklearn.neighbors._classification.KNeighborsClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "knn", "step_name": "knn"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,knn=sklearn.neighbors._classification.KNeighborsClassifier)(1)_verbosefalse
sklearn.neighbors._classification.KNeighborsClassifier(24)_algorithm"auto"
sklearn.neighbors._classification.KNeighborsClassifier(24)_leaf_size30
sklearn.neighbors._classification.KNeighborsClassifier(24)_metric"minkowski"
sklearn.neighbors._classification.KNeighborsClassifier(24)_metric_paramsnull
sklearn.neighbors._classification.KNeighborsClassifier(24)_n_jobsnull
sklearn.neighbors._classification.KNeighborsClassifier(24)_n_neighbors5
sklearn.neighbors._classification.KNeighborsClassifier(24)_p2
sklearn.neighbors._classification.KNeighborsClassifier(24)_weights"uniform"

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
0.617 ± 0.0059
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5897 ± 0.0055
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1768 ± 0.011
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1178 ± 0.0062
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.444 ± 0.0028
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4984 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5926 ± 0.0054
Cross-validation details (10-fold Crossvalidation)
Number of instances
98050
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5911 ± 0.0055
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5926 ± 0.0054
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9976 ± 0
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8909 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4992 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5094 ± 0.0026
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0205 ± 0.0051
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5878 ± 0.0055
Cross-validation details (10-fold Crossvalidation)