Run
10456812

Run 10456812

Task 3797 (Supervised Classification) socmob 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=sklearn.preprocessing._discret ization.KBinsDiscretizer,step_2=sklearn.ensemble._forest.RandomForestClassi fier)(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_alpha0.9939033297715806
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth100
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features10
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes401
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.766541926974416
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.8878998891801209
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.49653932919343124
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.22967538179027938
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.23092193016934598
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators2636
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.preprocessing._discretization.KBinsDiscretizer(1)_encode"onehot-dense"
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_n_bins51
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_strategy"quantile"
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,step_2=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,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=sklearn.preprocessing._discretization.KBinsDiscretizer,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

0.4953
Per class
Cross-validation details (10-fold Crossvalidation)
0.0006 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
0.3448 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
0.3451 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
0.7785 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
1156
Per class
Cross-validation details (10-fold Crossvalidation)
0.7785 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
0.7628 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
0.9992 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.4152 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.4152 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
1 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)