Run
10592138

Run 10592138

Task 9956 (Supervised Classification) one-hundred-plants-texture Uploaded 21-03-2023 by Takeaki Sakabe
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.tree._classes.DecisionTreeClassifier)(24)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)(24)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier)(24)_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)(24)_verbosefalse
sklearn.impute._base.SimpleImputer(42)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(42)_copytrue
sklearn.impute._base.SimpleImputer(42)_fill_valuenull
sklearn.impute._base.SimpleImputer(42)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(42)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(42)_strategy"mean"
sklearn.impute._base.SimpleImputer(42)_verbose"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(34)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(34)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(34)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(34)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(34)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(34)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(34)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(34)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(34)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(34)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(34)_random_state50161
sklearn.tree._classes.DecisionTreeClassifier(34)_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
0.7612 ± 0.0167
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5246
Per class
Kappa
0.5224 ± 0.0333
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5262 ± 0.033
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0095 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.0198 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5272 ± 0.033
Cross-validation details (10-fold Crossvalidation)
Number of instances
1599
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5303
Per class
Predictive accuracy
0.5272 ± 0.033
Cross-validation details (10-fold Crossvalidation)
Prior entropy
6.6438 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4776 ± 0.0333
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.0995 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0972 ± 0.0034
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9773 ± 0.0338
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5272 ± 0.0342
Cross-validation details (10-fold Crossvalidation)