Run
10592645

Run 10592645

Task 23 (Supervised Classification) cmc Uploaded 23-03-2023 by Takeaki Sakabe
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.neighbors._nearest_centroid.NearestCentroid)(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.impute._base.SimpleImputer(43)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(43)_copytrue
sklearn.impute._base.SimpleImputer(43)_fill_valuenull
sklearn.impute._base.SimpleImputer(43)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(43)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(43)_strategy"mean"
sklearn.impute._base.SimpleImputer(43)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._nearest_centroid.NearestCentroid)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._nearest_centroid.NearestCentroid)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._nearest_centroid.NearestCentroid)(1)_verbosefalse
sklearn.neighbors._nearest_centroid.NearestCentroid(1)_metric"euclidean"
sklearn.neighbors._nearest_centroid.NearestCentroid(1)_shrink_thresholdnull

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

0.5508 ± 0.0228
Per class
Cross-validation details (10-fold Crossvalidation)
0.3195 ± 0.0366
Per class
Cross-validation details (10-fold Crossvalidation)
0.1018 ± 0.0426
Cross-validation details (10-fold Crossvalidation)
0.1314 ± 0.044
Cross-validation details (10-fold Crossvalidation)
0.4182 ± 0.0203
Cross-validation details (10-fold Crossvalidation)
0.4308 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.3727 ± 0.0304
Cross-validation details (10-fold Crossvalidation)
1473
Per class
Cross-validation details (10-fold Crossvalidation)
0.4039 ± 0.0783
Per class
Cross-validation details (10-fold Crossvalidation)
0.3727 ± 0.0304
Cross-validation details (10-fold Crossvalidation)
1.539 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
0.9707 ± 0.0472
Cross-validation details (10-fold Crossvalidation)
0.4641 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.6467 ± 0.0156
Cross-validation details (10-fold Crossvalidation)
1.3934 ± 0.034
Cross-validation details (10-fold Crossvalidation)
0.4176 ± 0.0324
Cross-validation details (10-fold Crossvalidation)