Run
10464493

Run 10464493

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 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=sklearn.decomposition._truncated_svd.Trunc atedSVD,step_1=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)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.14639663438691597
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_features1
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes1196
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.8560562822404356
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.4008109676550408
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.1523181619628414
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.1657688513018579
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.46876431474943164
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators747
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scoretrue
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_startfalse
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_algorithm"arpack"
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_components9
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_iter22
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_random_state42
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_tol1.7075144408064993
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=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"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=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
0.4997
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0001 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4947 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5512 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5512 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5
Cross-validation details (10-fold Crossvalidation)