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4012

Run 4012

Task 6 (Supervised Classification) anneal Uploaded 01-03-2021 by Test Test
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Flow

sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.Col umnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._ base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEnco der,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=pa ssthrough),estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boos ting.HistGradientBoostingClassifier)(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.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD)(1)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"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(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD)(1)_verbosefalse
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"most_frequent"
sklearn.impute._base.SimpleImputer(7)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(8)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(8)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(8)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(8)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(8)_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_state37177
sklearn.decomposition._truncated_svd.TruncatedSVD(2)_tol0.0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_early_stopping"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_l2_regularization0.0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_learning_rate0.15
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_max_bins255
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_max_depthnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_max_iter50
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_max_leaf_nodes31
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_min_samples_leaf1
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_monotonic_cstnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_n_iter_no_change10
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_random_state5424
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_scoring"loss"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_tol1e-07
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_validation_fraction0.1
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(1)_warm_startfalse
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=passthrough),estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=passthrough),estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(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(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=passthrough),estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=passthrough)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=passthrough)(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=passthrough)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=passthrough)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=passthrough)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": {"oml-python:serialized_object": "function", "value": "__main__.cat"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cont", "step_name": "cont", "argument_1": {"oml-python:serialized_object": "function", "value": "__main__.cont"}}}]
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=passthrough)(1)_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.

17 Evaluation measures

Area under ROC curve
0.9845
Per class
Cross-validation details (33% Holdout set)
F measure
0.9372
Per class
Cross-validation details (33% Holdout set)
Kappa
0.8622
Cross-validation details (33% Holdout set)
KB relative information score
0.8344
Cross-validation details (33% Holdout set)
Mean absolute error
0.0212
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.141
Cross-validation details (33% Holdout set)
Weighted recall
0.9392
Cross-validation details (33% Holdout set)
Number of instances
296
Per class
Cross-validation details (33% Holdout set)
Precision
0.944
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.9392
Cross-validation details (33% Holdout set)
Prior entropy
1.309
Cross-validation details (33% Holdout set)
Relative absolute error
0.1502
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.2709
Cross-validation details (33% Holdout set)
Root mean squared error
0.1354
Cross-validation details (33% Holdout set)
Root relative squared error
0.4997
Cross-validation details (33% Holdout set)