Run
10591690

Run 10591690

Task 32 (Supervised Classification) pendigits Uploaded 10-11-2022 by Steve De Picker
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.tree._classes.DecisionTreeClassifier)(21)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.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier)(21)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier)(21)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier)(21)_verbosefalse
sklearn.impute._base.SimpleImputer(35)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(35)_copytrue
sklearn.impute._base.SimpleImputer(35)_fill_valuenull
sklearn.impute._base.SimpleImputer(35)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(35)_strategy"mean"
sklearn.impute._base.SimpleImputer(35)_verbose"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(30)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(30)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(30)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(30)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(30)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(30)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(30)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(30)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(30)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(30)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(30)_random_state31558
sklearn.tree._classes.DecisionTreeClassifier(30)_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

Area under ROC curve
0.9799 ± 0.0034
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9638 ± 0.0061
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9598 ± 0.0068
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9621 ± 0.0064
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0072 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9638 ± 0.0061
Cross-validation details (10-fold Crossvalidation)
Number of instances
10992
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9638 ± 0.0059
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9638 ± 0.0061
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3208 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0402 ± 0.0068
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0851 ± 0.0071
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.2837 ± 0.0236
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9638 ± 0.0062
Cross-validation details (10-fold Crossvalidation)