Run
10457907

Run 10457907

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.components.feature_prep rocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_ 2=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.ensemble._forest.RandomForestClassifier(2)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.618896783934749
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth9
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features5
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes1637
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.2799958763442346
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.6236289886702749
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.11469771021941025
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.029348399848819817
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.06987652781663994
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators767
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
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_2=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_2=sklearn.ensemble._forest.RandomForestClassifier)(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.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_2=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.

16 Evaluation measures

Area under ROC curve
0.499
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0001 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4441 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4441 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.3464 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Number of instances
4177
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.3464 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.584 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4712 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4712 ± 0
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.3333
Cross-validation details (10-fold Crossvalidation)