Run
10437840

Run 10437840

Task 3484 (Supervised Classification) oil_spill Uploaded 03-03-2020 by Fares Gaaloul
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Flow

sklearn.pipeline.Pipeline(Imputer=sklearn.impute.SimpleImputer,fs=sklearn.f eature_selection.univariate_selection.SelectPercentile,rf=sklearn.ensemble. forest.RandomForestClassifier)(2)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 to None.
sklearn.impute.SimpleImputer(15)_copytrue
sklearn.impute.SimpleImputer(15)_fill_valuenull
sklearn.impute.SimpleImputer(15)_missing_valuesNaN
sklearn.impute.SimpleImputer(15)_strategy"constant"
sklearn.impute.SimpleImputer(15)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(61)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(61)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(61)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(61)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(61)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(61)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(61)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(61)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(61)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(61)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(61)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(61)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(61)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(61)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(61)_random_state31461
sklearn.ensemble.forest.RandomForestClassifier(61)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(61)_warm_startfalse
sklearn.pipeline.Pipeline(Imputer=sklearn.impute.SimpleImputer,fs=sklearn.feature_selection.univariate_selection.SelectPercentile,rf=sklearn.ensemble.forest.RandomForestClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(Imputer=sklearn.impute.SimpleImputer,fs=sklearn.feature_selection.univariate_selection.SelectPercentile,rf=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "fs", "step_name": "fs"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "rf", "step_name": "rf"}}]
sklearn.feature_selection.univariate_selection.SelectPercentile(4)_percentile80
sklearn.feature_selection.univariate_selection.SelectPercentile(4)_score_func{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection.univariate_selection.f_classif"}

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
0.9387 ± 0.0396
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9519 ± 0.0171
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.319 ± 0.2842
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.352 ± 0.2013
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0619 ± 0.006
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.0846 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9616 ± 0.0125
Cross-validation details (10-fold Crossvalidation)
Number of instances
937
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9534 ± 0.0233
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9616 ± 0.0125
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.2593 ± 0.0147
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7319 ± 0.0669
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2046 ± 0.0071
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1715 ± 0.0229
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8386 ± 0.1047
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6075 ± 0.1235
Cross-validation details (10-fold Crossvalidation)