Run
10454565

Run 10454565

Task 3735 (Supervised Classification) pollen Uploaded 19-05-2020 by Marc Zöller
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • automl_meta_features openml-python Sklearn_0.22.1.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler ,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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(1)_copyfalse
sklearn.preprocessing._data.StandardScaler(1)_with_meantrue
sklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_algorithm"SAMME"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate0.000448327411303159
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators1418
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state42
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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.preprocessing._data.StandardScaler,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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.

16 Evaluation measures

0.4994
Per class
Cross-validation details (10-fold Crossvalidation)
-0 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
3848
Per class
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
1
Cross-validation details (10-fold Crossvalidation)
1 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)
0.5508 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
1.1016 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)