Run
10590943

Run 10590943

Task 9983 (Supervised Classification) eeg-eye-state 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.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(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.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbosefalse
sklearn.ensemble._weight_boosting.AdaBoostClassifier(3)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(3)_base_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(3)_learning_rate1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(3)_n_estimators50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(3)_random_state38778

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

Area under ROC curve
0.8171 ± 0.0087
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.741 ± 0.0104
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4753 ± 0.021
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0025 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4934 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7422 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7415 ± 0.0107
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7422 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9972 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4938 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9929 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.736 ± 0.0103
Cross-validation details (10-fold Crossvalidation)