Run
10587765

Run 10587765

Task 6 (Supervised Classification) letter Uploaded 17-04-2022 by Tan Tran
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Flow

sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer .ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncode r,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensembl e._forest.RandomForestClassifier)(3)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.impute._base.SimpleImputer(28)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(28)_copytrue
sklearn.impute._base.SimpleImputer(28)_fill_valuenull
sklearn.impute._base.SimpleImputer(28)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(28)_strategy"median"
sklearn.impute._base.SimpleImputer(28)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(30)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(30)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(30)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(30)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(30)_sparsefalse
sklearn.ensemble._forest.RandomForestClassifier(13)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(13)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(13)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(13)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(13)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(13)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(13)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(13)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(13)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(13)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(13)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(13)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(13)_n_estimators10
sklearn.ensemble._forest.RandomForestClassifier(13)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(13)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(13)_random_state26984
sklearn.ensemble._forest.RandomForestClassifier(13)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(13)_warm_startfalse
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(3)_memorynull
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(3)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Preprocessing", "step_name": "Preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Classifier", "step_name": "Classifier"}}]
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(3)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(3)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(3)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(3)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(3)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(3)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "categorical", "step_name": "categorical", "argument_1": []}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "continuous", "step_name": "continuous", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7]}}]
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(3)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(3)_verbose_feature_names_outtrue

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
0.9188 ± 0.0043
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6334 ± 0.0117
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6179 ± 0.0122
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6757 ± 0.0075
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0368 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.074 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6327 ± 0.0117
Cross-validation details (10-fold Crossvalidation)
Number of instances
20000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6358 ± 0.0118
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6327 ± 0.0117
Cross-validation details (10-fold Crossvalidation)
Prior entropy
4.6998 ± 0
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.497 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.1923 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1396 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7257 ± 0.0089
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6321 ± 0.0117
Cross-validation details (10-fold Crossvalidation)