Run
10462344

Run 10462344

Task 3891 (Supervised Classification) gina_agnostic Uploaded 21-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAggl omeration,step_1=sklearn.naive_bayes.MultinomialNB)(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``.
sklearn.naive_bayes.MultinomialNB(6)_alpha4.1434175365694165e-05
sklearn.naive_bayes.MultinomialNB(6)_class_priornull
sklearn.naive_bayes.MultinomialNB(6)_fit_priorfalse
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_affinity"euclidean"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_compute_full_treetrue
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_connectivitynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_distance_thresholdnull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_linkage"complete"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_memorynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_n_clusters148
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_pooling_func{"oml-python:serialized_object": "function", "value": "numpy.mean"}
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.naive_bayes.MultinomialNB)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.naive_bayes.MultinomialNB)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.naive_bayes.MultinomialNB)(1)_verbosefalse

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

0.841 ± 0.0167
Per class
Cross-validation details (10-fold Crossvalidation)
0.7978 ± 0.0153
Per class
Cross-validation details (10-fold Crossvalidation)
0.5954 ± 0.0307
Cross-validation details (10-fold Crossvalidation)
0.5958 ± 0.0306
Cross-validation details (10-fold Crossvalidation)
0.202 ± 0.0153
Cross-validation details (10-fold Crossvalidation)
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
0.7979 ± 0.0154
Cross-validation details (10-fold Crossvalidation)
3468
Per class
Cross-validation details (10-fold Crossvalidation)
0.798 ± 0.0158
Per class
Cross-validation details (10-fold Crossvalidation)
0.7979 ± 0.0154
Cross-validation details (10-fold Crossvalidation)
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.4041 ± 0.0306
Cross-validation details (10-fold Crossvalidation)
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
0.4491 ± 0.0176
Cross-validation details (10-fold Crossvalidation)
0.8983 ± 0.0351
Cross-validation details (10-fold Crossvalidation)
0.7976 ± 0.0153
Cross-validation details (10-fold Crossvalidation)