Run
10593676

Run 10593676

Task 361444 (Supervised Classification) phoneme Uploaded 09-05-2023 by Takeaki Sakabe
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.ensemble._forest.RandomForestClassifier)(3)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.impute._base.SimpleImputer(43)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(43)_copytrue
sklearn.impute._base.SimpleImputer(43)_fill_valuenull
sklearn.impute._base.SimpleImputer(43)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(43)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(43)_strategy"mean"
sklearn.impute._base.SimpleImputer(43)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._forest.RandomForestClassifier)(3)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._forest.RandomForestClassifier)(3)_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.ensemble._forest.RandomForestClassifier)(3)_verbosefalse
sklearn.ensemble._forest.RandomForestClassifier(28)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(28)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(28)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(28)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(28)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(28)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(28)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(28)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(28)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(28)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(28)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(28)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(28)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(28)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(28)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(28)_random_state59399
sklearn.ensemble._forest.RandomForestClassifier(28)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(28)_warm_startfalse

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
0.9498 ± 0.0038
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
F measure
0.8907 ± 0.0063
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Kappa
0.7353 ± 0.0154
Cross-validation details (5 times 2-fold Crossvalidation)
KB relative information score
0.5844 ± 0.0072
Cross-validation details (5 times 2-fold Crossvalidation)
Mean absolute error
0.1774 ± 0.0022
Cross-validation details (5 times 2-fold Crossvalidation)
Mean prior absolute error
0.4147 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Weighted recall
0.8913 ± 0.0062
Cross-validation details (5 times 2-fold Crossvalidation)
Number of instances
27020
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Precision
0.8904 ± 0.0064
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Predictive accuracy
0.8913 ± 0.0062
Cross-validation details (5 times 2-fold Crossvalidation)
Prior entropy
0.8732 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Relative absolute error
0.4278 ± 0.0053
Cross-validation details (5 times 2-fold Crossvalidation)
Root mean prior squared error
0.4554 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Root mean squared error
0.2831 ± 0.0042
Cross-validation details (5 times 2-fold Crossvalidation)
Root relative squared error
0.6217 ± 0.0092
Cross-validation details (5 times 2-fold Crossvalidation)
Unweighted recall
0.8641 ± 0.0084
Cross-validation details (5 times 2-fold Crossvalidation)