Run
10454469

Run 10454469

Task 3735 (Supervised Classification) pollen 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=sklearn.preprocessing._data.StandardScaler ,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.preprocessing._data.StandardScaler(1)_copyfalse
sklearn.preprocessing._data.StandardScaler(1)_with_meantrue
sklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
sklearn.ensemble._forest.RandomForestClassifier(2)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.2499771303613878
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth3
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes2304
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.5124286245244082
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.47929026007343706
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.2859565312349867
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.057813459635944385
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.07187276457419661
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators1999
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.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,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.preprocessing._data.StandardScaler,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.

18 Evaluation measures

Area under ROC curve
0.4988
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4939
Per class
Kappa
-0.0021
KB relative information score
-0 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.5 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.5
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.499 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Number of instances
3848
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4989
Per class
Predictive accuracy
0.499 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.5
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5 ± 0
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.499
Cross-validation details (10-fold Crossvalidation)