Run
10436661

Run 10436661

Task 23 (Supervised Classification) cmc Uploaded 28-01-2020 by Ding Dong
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,decisi ontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(3)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(simpleimputer=sklearn.impute.SimpleImputer,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(3)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(3)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.impute.SimpleImputer(16)_copytrue
sklearn.impute.SimpleImputer(16)_fill_valuenull
sklearn.impute.SimpleImputer(16)_missing_valuesNaN
sklearn.impute.SimpleImputer(16)_strategy"mean"
sklearn.impute.SimpleImputer(16)_verbose0
sklearn.tree.tree.DecisionTreeClassifier(61)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(61)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(61)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(61)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(61)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(61)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(61)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(61)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(61)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(61)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(61)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(61)_random_state35189
sklearn.tree.tree.DecisionTreeClassifier(61)_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

0.6098 ± 0.0297
Per class
Cross-validation details (10-fold Crossvalidation)
0.4855 ± 0.0365
Per class
Cross-validation details (10-fold Crossvalidation)
0.2048 ± 0.0584
Cross-validation details (10-fold Crossvalidation)
0.2673 ± 0.0494
Cross-validation details (10-fold Crossvalidation)
0.3423 ± 0.0228
Cross-validation details (10-fold Crossvalidation)
0.4308 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.4861 ± 0.0361
Cross-validation details (10-fold Crossvalidation)
1473
Per class
Cross-validation details (10-fold Crossvalidation)
0.4852 ± 0.0382
Per class
Cross-validation details (10-fold Crossvalidation)
0.4861 ± 0.0361
Cross-validation details (10-fold Crossvalidation)
1.539 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
0.7945 ± 0.0528
Cross-validation details (10-fold Crossvalidation)
0.4641 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.5783 ± 0.0196
Cross-validation details (10-fold Crossvalidation)
1.2461 ± 0.0423
Cross-validation details (10-fold Crossvalidation)
0.4673 ± 0.0387
Cross-validation details (10-fold Crossvalidation)