Run
10436656

Run 10436656

Task 14 (Supervised Classification) mfeat-fourier Uploaded 28-01-2020 by Sruthi Radhakrishnan
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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

0.8603 ± 0.0208
Per class
Cross-validation details (10-fold Crossvalidation)
0.7495 ± 0.0356
Per class
Cross-validation details (10-fold Crossvalidation)
0.7206 ± 0.0417
Cross-validation details (10-fold Crossvalidation)
0.737 ± 0.0392
Cross-validation details (10-fold Crossvalidation)
0.0503 ± 0.0075
Cross-validation details (10-fold Crossvalidation)
0.18
Cross-validation details (10-fold Crossvalidation)
0.7485 ± 0.0375
Cross-validation details (10-fold Crossvalidation)
2000
Per class
Cross-validation details (10-fold Crossvalidation)
0.7507 ± 0.0354
Per class
Cross-validation details (10-fold Crossvalidation)
0.7485 ± 0.0375
Cross-validation details (10-fold Crossvalidation)
3.3219
Cross-validation details (10-fold Crossvalidation)
0.2794 ± 0.0417
Cross-validation details (10-fold Crossvalidation)
0.3
Cross-validation details (10-fold Crossvalidation)
0.2243 ± 0.0167
Cross-validation details (10-fold Crossvalidation)
0.7476 ± 0.0555
Cross-validation details (10-fold Crossvalidation)
0.7485 ± 0.0375
Cross-validation details (10-fold Crossvalidation)