Run
10437824

Run 10437824

Task 35 (Supervised Classification) dermatology 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
0.9991 ± 0.0016
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9753 ± 0.0158
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9692 ± 0.0196
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9356 ± 0.0202
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0282 ± 0.0071
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2664 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9754 ± 0.0157
Cross-validation details (10-fold Crossvalidation)
Number of instances
366
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9753 ± 0.0153
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9754 ± 0.0157
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.4327 ± 0.0093
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1059 ± 0.0266
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3649 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0893 ± 0.02
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.2448 ± 0.0547
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9671 ± 0.025
Cross-validation details (10-fold Crossvalidation)