Run
10592546

Run 10592546

Task 36 (Supervised Classification) segment 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

0.8371 ± 0.0169
Per class
Cross-validation details (10-fold Crossvalidation)
0.7085 ± 0.0258
Per class
Cross-validation details (10-fold Crossvalidation)
0.6742 ± 0.0339
Cross-validation details (10-fold Crossvalidation)
0.6987 ± 0.0313
Cross-validation details (10-fold Crossvalidation)
0.0798 ± 0.0083
Cross-validation details (10-fold Crossvalidation)
0.2449
Cross-validation details (10-fold Crossvalidation)
0.7208 ± 0.029
Cross-validation details (10-fold Crossvalidation)
2310
Per class
Cross-validation details (10-fold Crossvalidation)
0.7644 ± 0.0248
Per class
Cross-validation details (10-fold Crossvalidation)
0.7208 ± 0.029
Cross-validation details (10-fold Crossvalidation)
2.8074
Cross-validation details (10-fold Crossvalidation)
0.3258 ± 0.0339
Cross-validation details (10-fold Crossvalidation)
0.3499
Cross-validation details (10-fold Crossvalidation)
0.2824 ± 0.0148
Cross-validation details (10-fold Crossvalidation)
0.8072 ± 0.0423
Cross-validation details (10-fold Crossvalidation)
0.7208 ± 0.029
Cross-validation details (10-fold Crossvalidation)