Run
10593803

Run 10593803

Task 361110 (Supervised Classification) electricity Uploaded 16-05-2023 by Eduardo Denadai
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Flow

sklearn.pipeline.Pipeline(decisiontreeclassifier=sklearn.tree._classes.Deci sionTreeClassifier)(1)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.tree._classes.DecisionTreeClassifier(38)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(38)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(38)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(38)_max_depth1
sklearn.tree._classes.DecisionTreeClassifier(38)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(38)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(38)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(38)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(38)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(38)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(38)_random_state41059
sklearn.tree._classes.DecisionTreeClassifier(38)_splitter"best"
sklearn.pipeline.Pipeline(decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.pipeline.Pipeline(decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbosefalse

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
0.7296 ± 0.005
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7251 ± 0.005
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4647 ± 0.01
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2763 ± 0.0054
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3793 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.5
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7323 ± 0.005
Cross-validation details (10-fold Crossvalidation)
Number of instances
38474
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7597 ± 0.0065
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7323 ± 0.005
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7587 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.5
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4355 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.871 ± 0.0064
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7323 ± 0.005
Cross-validation details (10-fold Crossvalidation)