Run
10560277

Run 10560277

Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklear n.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.Rand omForestClassifier)(2)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 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 to None.
sklearn.preprocessing.data.PolynomialFeatures(6)_degree2
sklearn.preprocessing.data.PolynomialFeatures(6)_include_biastrue
sklearn.preprocessing.data.PolynomialFeatures(6)_interaction_onlyfalse
sklearn.decomposition.pca.PCA(11)_copytrue
sklearn.decomposition.pca.PCA(11)_iterated_power"auto"
sklearn.decomposition.pca.PCA(11)_n_componentsnull
sklearn.decomposition.pca.PCA(11)_random_state7943
sklearn.decomposition.pca.PCA(11)_svd_solver"auto"
sklearn.decomposition.pca.PCA(11)_tol0.0
sklearn.decomposition.pca.PCA(11)_whitenfalse
sklearn.ensemble.forest.RandomForestClassifier(67)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(67)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(67)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(67)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(67)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(67)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(67)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(67)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(67)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(67)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(67)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(67)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(67)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(67)_random_state5832
sklearn.ensemble.forest.RandomForestClassifier(67)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(67)_warm_startfalse
sklearn.preprocessing.imputation.Imputer(52)_axis0
sklearn.preprocessing.imputation.Imputer(52)_copytrue
sklearn.preprocessing.imputation.Imputer(52)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(52)_strategy"median"
sklearn.preprocessing.imputation.Imputer(52)_verbose0
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "polynomialfeatures", "step_name": "polynomialfeatures"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "pca", "step_name": "pca"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]

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

0.6713 ± 0.0483
Per class
Cross-validation details (10-fold Crossvalidation)
0.7317 ± 0.05
Per class
Cross-validation details (10-fold Crossvalidation)
0.2201 ± 0.1519
Cross-validation details (10-fold Crossvalidation)
0.0797 ± 0.0855
Cross-validation details (10-fold Crossvalidation)
0.3059 ± 0.0221
Cross-validation details (10-fold Crossvalidation)
0.363 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.7527 ± 0.0394
Cross-validation details (10-fold Crossvalidation)
748
Per class
Cross-validation details (10-fold Crossvalidation)
0.7236 ± 0.0592
Per class
Cross-validation details (10-fold Crossvalidation)
0.7527 ± 0.0394
Cross-validation details (10-fold Crossvalidation)
0.7916 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
0.8425 ± 0.0616
Cross-validation details (10-fold Crossvalidation)
0.4258 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.4324 ± 0.0255
Cross-validation details (10-fold Crossvalidation)
1.0153 ± 0.061
Cross-validation details (10-fold Crossvalidation)
0.5962 ± 0.0685
Cross-validation details (10-fold Crossvalidation)