Run
10559925

Run 10559925

Task 6 (Supervised Classification) letter Uploaded 07-07-2021 by Salomé Maltese
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  • openml-python Sklearn_0.22.2.post1.
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Flow

sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.Col umnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preproce ssing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated _svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer),estimator=sklea rn.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.ensemble._forest.RandomForestClassifier(9)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(9)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(9)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(9)_max_depth10
sklearn.ensemble._forest.RandomForestClassifier(9)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(9)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(9)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(9)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(9)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(9)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(9)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_n_estimators50
sklearn.ensemble._forest.RandomForestClassifier(9)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(9)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(9)_random_state59026
sklearn.ensemble._forest.RandomForestClassifier(9)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(9)_warm_startfalse
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer),estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer),estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "transform", "step_name": "transform"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer),estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cont", "step_name": "cont", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}]
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_verbosefalse
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD)(1)_memorynull
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "truncatedsvd", "step_name": "truncatedsvd"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD)(1)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(27)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(27)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(27)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(27)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(27)_sparsefalse
sklearn.decomposition._truncated_svd.TruncatedSVD(2)_algorithm"randomized"
sklearn.decomposition._truncated_svd.TruncatedSVD(2)_n_components2
sklearn.decomposition._truncated_svd.TruncatedSVD(2)_n_iter5
sklearn.decomposition._truncated_svd.TruncatedSVD(2)_random_state28715
sklearn.decomposition._truncated_svd.TruncatedSVD(2)_tol0.0
sklearn.impute._base.SimpleImputer(26)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(26)_copytrue
sklearn.impute._base.SimpleImputer(26)_fill_valuenull
sklearn.impute._base.SimpleImputer(26)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(26)_strategy"median"
sklearn.impute._base.SimpleImputer(26)_verbose0

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
0.9934 ± 0.0003
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8653 ± 0.0084
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8567 ± 0.009
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.7376 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0344 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.074 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8623 ± 0.0086
Cross-validation details (10-fold Crossvalidation)
Number of instances
20000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8775 ± 0.0068
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8623 ± 0.0086
Cross-validation details (10-fold Crossvalidation)
Prior entropy
4.6998 ± 0
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4653 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.1923 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1128 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5867 ± 0.0044
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8616 ± 0.0087
Cross-validation details (10-fold Crossvalidation)