Run
10438394

Run 10438394

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)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(62)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(62)_criterion"entropy"
sklearn.ensemble.forest.RandomForestClassifier(62)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(62)_max_features0.16333922356751462
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_leaf4
sklearn.ensemble.forest.RandomForestClassifier(62)_min_samples_split18
sklearn.ensemble.forest.RandomForestClassifier(62)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(62)_n_estimators300
sklearn.ensemble.forest.RandomForestClassifier(62)_n_jobs1
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
0.9507 ± 0.0099
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.89 ± 0.0159
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7335 ± 0.0392
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.533 ± 0.0228
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2012 ± 0.008
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4147 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8906 ± 0.0154
Cross-validation details (10-fold Crossvalidation)
Number of instances
5404
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8897 ± 0.0162
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8906 ± 0.0154
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8732 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4851 ± 0.0193
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
0.2914 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6399 ± 0.0244
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8629 ± 0.0228
Cross-validation details (10-fold Crossvalidation)