Run
10438385

Run 10438385

Task 9952 (Supervised Classification) phoneme Uploaded 05-04-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassi fier=sklearn.ensemble.forest.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.impute._base.SimpleImputer(13)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(13)_copytrue
sklearn.impute._base.SimpleImputer(13)_fill_valuenull
sklearn.impute._base.SimpleImputer(13)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(13)_strategy"median"
sklearn.impute._base.SimpleImputer(13)_verbose0
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbosefalse
sklearn.preprocessing.data.StandardScaler(39)_copytrue
sklearn.preprocessing.data.StandardScaler(39)_with_meantrue
sklearn.preprocessing.data.StandardScaler(39)_with_stdtrue
sklearn.ensemble.forest.RandomForestClassifier(62)_bootstrapfalse
sklearn.ensemble.forest.RandomForestClassifier(62)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(62)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(62)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(62)_max_features0.16336813614519885
sklearn.ensemble.forest.RandomForestClassifier(62)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(62)_min_impurity_decrease1e-07
sklearn.ensemble.forest.RandomForestClassifier(62)_min_impurity_split0
sklearn.ensemble.forest.RandomForestClassifier(62)_min_samples_leaf2
sklearn.ensemble.forest.RandomForestClassifier(62)_min_samples_split3
sklearn.ensemble.forest.RandomForestClassifier(62)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(62)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(62)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(62)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(62)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(62)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(62)_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
0.9651 ± 0.0094
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9126 ± 0.0134
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7885 ± 0.0327
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6351 ± 0.0257
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1585 ± 0.0091
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4147 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9128 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
Number of instances
5404
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9124 ± 0.0136
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9128 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8732 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3822 ± 0.0219
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4554 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2597 ± 0.0147
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5703 ± 0.0321
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8919 ± 0.0194
Cross-validation details (10-fold Crossvalidation)