OpenML
10594040

Run 10594040

Task 361916 (Supervised Classification) appendicitis_test_edsa Uploaded 07-08-2023 by said edsa
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Flow

sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._vari ance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._f orest.RandomForestClassifier)(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.ensemble._forest.RandomForestClassifier(32)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(32)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(32)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(32)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(32)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(32)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(32)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(32)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(32)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(32)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(32)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(32)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(32)_random_state2281
sklearn.ensemble._forest.RandomForestClassifier(32)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(32)_warm_startfalse
sklearn.feature_selection._variance_threshold.VarianceThreshold(12)_threshold0.0
sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(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
0.8188 ± 0.1804
Per class
Cross-validation details (10 times 10-fold Crossvalidation)
F measure
0.8675 ± 0.0906
Per class
Cross-validation details (10 times 10-fold Crossvalidation)
Kappa
0.5664 ± 0.3448
Cross-validation details (10 times 10-fold Crossvalidation)
KB relative information score
0.3369 ± 0.2528
Cross-validation details (10 times 10-fold Crossvalidation)
Mean absolute error
0.1957 ± 0.06
Cross-validation details (10 times 10-fold Crossvalidation)
Mean prior absolute error
0.3211 ± 0.0157
Cross-validation details (10 times 10-fold Crossvalidation)
Weighted recall
0.8736 ± 0.0849
Cross-validation details (10 times 10-fold Crossvalidation)
Number of instances
1060
Per class
Cross-validation details (10 times 10-fold Crossvalidation)
Precision
0.8663 ± 0.0987
Per class
Cross-validation details (10 times 10-fold Crossvalidation)
Predictive accuracy
0.8736 ± 0.0849
Cross-validation details (10 times 10-fold Crossvalidation)
Prior entropy
0.7183 ± 0.052
Cross-validation details (10 times 10-fold Crossvalidation)
Relative absolute error
0.6093 ± 0.1869
Cross-validation details (10 times 10-fold Crossvalidation)
Root mean prior squared error
0.3986 ± 0.0187
Cross-validation details (10 times 10-fold Crossvalidation)
Root mean squared error
0.3293 ± 0.0974
Cross-validation details (10 times 10-fold Crossvalidation)
Root relative squared error
0.826 ± 0.2447
Cross-validation details (10 times 10-fold Crossvalidation)
Unweighted recall
0.7598 ± 0.1758
Cross-validation details (10 times 10-fold Crossvalidation)