Run
10457895

Run 10457895

Task 9900 (Supervised Classification) abalone Uploaded 19-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEs timator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_2=s klearn.discriminant_analysis.LinearDiscriminantAnalysis)(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(2)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.06797738105121776
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features3
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes3357
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.2451847511282583
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.22627423962905827
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.0914736437733425
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.2819207742759575
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.33949765897881484
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators1151
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_startfalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components166
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.30825329079213415
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"eigen"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.012504000829593409
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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
0.7803 ± 0.0168
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5683 ± 0.021
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3751 ± 0.031
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3274 ± 0.0156
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3325 ± 0.0069
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4441 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5851 ± 0.0207
Cross-validation details (10-fold Crossvalidation)
Number of instances
4177
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5668 ± 0.024
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5851 ± 0.0207
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.584 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7487 ± 0.0156
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4712 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4129 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8763 ± 0.0137
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5792 ± 0.0206
Cross-validation details (10-fold Crossvalidation)