Run
10560510

Run 10560510

Task 15 (Supervised Classification) breast-w Uploaded 14-08-2021 by Sergey Redyuk
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.preprocessing.imputation.Imputer, decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(9)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 to None.
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(9)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(9)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.preprocessing.imputation.Imputer(54)_axis0
sklearn.preprocessing.imputation.Imputer(54)_copytrue
sklearn.preprocessing.imputation.Imputer(54)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(54)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(54)_verbose0
sklearn.tree.tree.DecisionTreeClassifier(68)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(68)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(68)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(68)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(68)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(68)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(68)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(68)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(68)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(68)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(68)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(68)_random_state2890
sklearn.tree.tree.DecisionTreeClassifier(68)_splitter"best"

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

0.9348 ± 0.0324
Per class
Cross-validation details (10-fold Crossvalidation)
0.944 ± 0.0275
Per class
Cross-validation details (10-fold Crossvalidation)
0.8757 ± 0.0614
Cross-validation details (10-fold Crossvalidation)
0.8712 ± 0.0632
Cross-validation details (10-fold Crossvalidation)
0.0558 ± 0.0273
Cross-validation details (10-fold Crossvalidation)
0.4519 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.9442 ± 0.0273
Cross-validation details (10-fold Crossvalidation)
699
Per class
Cross-validation details (10-fold Crossvalidation)
0.944 ± 0.0275
Per class
Cross-validation details (10-fold Crossvalidation)
0.9442 ± 0.0273
Cross-validation details (10-fold Crossvalidation)
0.9293 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
0.1235 ± 0.0605
Cross-validation details (10-fold Crossvalidation)
0.4753 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
0.2362 ± 0.0598
Cross-validation details (10-fold Crossvalidation)
0.497 ± 0.1259
Cross-validation details (10-fold Crossvalidation)
0.9348 ± 0.0324
Cross-validation details (10-fold Crossvalidation)