Run
10593280

Run 10593280

Task 32 (Supervised Classification) pendigits Uploaded 01-04-2023 by Ribeiro Pedro
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Flow

sklearn.pipeline.Pipeline(scale=sklearn.preprocessing._data.MinMaxScaler,es timator=sklearn.tree._classes.DecisionTreeClassifier)(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(35)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(35)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(35)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(35)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(35)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(35)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(35)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(35)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(35)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(35)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(35)_random_state22450
sklearn.tree._classes.DecisionTreeClassifier(35)_splitter"best"
sklearn.pipeline.Pipeline(scale=sklearn.preprocessing._data.MinMaxScaler,estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(scale=sklearn.preprocessing._data.MinMaxScaler,estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "scale", "step_name": "scale"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(scale=sklearn.preprocessing._data.MinMaxScaler,estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbosefalse
sklearn.preprocessing._data.MinMaxScaler(4)_clipfalse
sklearn.preprocessing._data.MinMaxScaler(4)_copytrue
sklearn.preprocessing._data.MinMaxScaler(4)_feature_range[0, 1]

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

0.9791 ± 0.0046
Per class
Cross-validation details (10-fold Crossvalidation)
0.9624 ± 0.0082
Per class
Cross-validation details (10-fold Crossvalidation)
0.9582 ± 0.0091
Cross-validation details (10-fold Crossvalidation)
0.9607 ± 0.0086
Cross-validation details (10-fold Crossvalidation)
0.0075 ± 0.0016
Cross-validation details (10-fold Crossvalidation)
0.18 ± 0
Cross-validation details (10-fold Crossvalidation)
0.9624 ± 0.0082
Cross-validation details (10-fold Crossvalidation)
10992
Per class
Cross-validation details (10-fold Crossvalidation)
0.9624 ± 0.0082
Per class
Cross-validation details (10-fold Crossvalidation)
0.9624 ± 0.0082
Cross-validation details (10-fold Crossvalidation)
3.3208 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.0418 ± 0.0091
Cross-validation details (10-fold Crossvalidation)
0.3 ± 0
Cross-validation details (10-fold Crossvalidation)
0.0867 ± 0.0092
Cross-validation details (10-fold Crossvalidation)
0.289 ± 0.0307
Cross-validation details (10-fold Crossvalidation)
0.9624 ± 0.0083
Cross-validation details (10-fold Crossvalidation)