Run
10715

Run 10715

Task 6 (Supervised Classification) anneal Uploaded 07-11-2019 by Test Test
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(5)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(16)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(16)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(16)_criterion"entropy"
sklearn.ensemble.forest.RandomForestClassifier(16)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(16)_max_features0.2
sklearn.ensemble.forest.RandomForestClassifier(16)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(16)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_n_estimators16
sklearn.ensemble.forest.RandomForestClassifier(16)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(16)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(16)_random_state42
sklearn.ensemble.forest.RandomForestClassifier(16)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(16)_warm_startfalse
sklearn.impute._base.SimpleImputer(7)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(7)_copytrue
sklearn.impute._base.SimpleImputer(7)_fill_valuenull
sklearn.impute._base.SimpleImputer(7)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(7)_strategy"median"
sklearn.impute._base.SimpleImputer(7)_verbose0
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(5)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(5)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(5)_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.

15 Evaluation measures

Area under ROC curve
0.9861
Per class
Cross-validation details (33% Holdout set)
Kappa
0.8465
Cross-validation details (33% Holdout set)
KB relative information score
0.8209
Cross-validation details (33% Holdout set)
Mean absolute error
0.0358
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.141
Cross-validation details (33% Holdout set)
Weighted recall
0.9324
Cross-validation details (33% Holdout set)
Number of instances
296
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.9324
Cross-validation details (33% Holdout set)
Prior entropy
1.309
Cross-validation details (33% Holdout set)
Relative absolute error
0.2538
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.2709
Cross-validation details (33% Holdout set)
Root mean squared error
0.1202
Cross-validation details (33% Holdout set)
Root relative squared error
0.4437
Cross-validation details (33% Holdout set)