Run
10590868

Run 10590868

Task 3954 (Supervised Classification) MagicTelescope Uploaded 11-10-2022 by VAIBHAV JAISWAL
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Flow

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklea rn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardSc aler),model=sklearn.naive_bayes.GaussianNB)(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.preprocessing._data.StandardScaler(11)_copytrue
sklearn.preprocessing._data.StandardScaler(11)_with_meantrue
sklearn.preprocessing._data.StandardScaler(11)_with_stdtrue
sklearn.impute._base.SimpleImputer(30)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(30)_copytrue
sklearn.impute._base.SimpleImputer(30)_fill_valuenull
sklearn.impute._base.SimpleImputer(30)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(30)_strategy"mean"
sklearn.impute._base.SimpleImputer(30)_verbose0
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_memorynull
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_verbosefalse
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.naive_bayes.GaussianNB)(1)_memorynull
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.naive_bayes.GaussianNB)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "numerical", "step_name": "numerical"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "model", "step_name": "model"}}]
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.naive_bayes.GaussianNB)(1)_verbosefalse
sklearn.naive_bayes.GaussianNB(23)_priorsnull
sklearn.naive_bayes.GaussianNB(23)_var_smoothing1e-09

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

0.757 ± 0.0093
Per class
Cross-validation details (10-fold Crossvalidation)
0.7004 ± 0.0072
Per class
Cross-validation details (10-fold Crossvalidation)
0.3288 ± 0.0162
Cross-validation details (10-fold Crossvalidation)
0.3672 ± 0.0148
Cross-validation details (10-fold Crossvalidation)
0.279 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
0.456 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.7268 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
19020
Per class
Cross-validation details (10-fold Crossvalidation)
0.7233 ± 0.0106
Per class
Cross-validation details (10-fold Crossvalidation)
0.7268 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
0.9355 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.6118 ± 0.0139
Cross-validation details (10-fold Crossvalidation)
0.4775 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.4859 ± 0.0062
Cross-validation details (10-fold Crossvalidation)
1.0177 ± 0.0129
Cross-validation details (10-fold Crossvalidation)
0.6468 ± 0.0071
Cross-validation details (10-fold Crossvalidation)