Run
10714

Run 10714

Task 96 (Supervised Classification) credit-a Uploaded 29-11-2022 by Test Test
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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)(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.preprocessing._encoders.OneHotEncoder(12)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(12)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(12)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(12)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(12)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(12)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(12)_sparsefalse
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_depthnull
sklearn.ensemble._forest.RandomForestClassifier(9)_max_features"sqrt"
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_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_estimators10
sklearn.ensemble._forest.RandomForestClassifier(9)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(9)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(9)_random_state19455
sklearn.ensemble._forest.RandomForestClassifier(9)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(9)_warm_startfalse
sklearn.impute._base.SimpleImputer(11)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(11)_copytrue
sklearn.impute._base.SimpleImputer(11)_fill_valuenull
sklearn.impute._base.SimpleImputer(11)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(11)_strategy"median"
sklearn.impute._base.SimpleImputer(11)_verbose"deprecated"
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_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)(1)_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)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "categorical", "step_name": "categorical", "argument_1": [0, 3, 4, 5, 6, 8, 9, 11, 12]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "continuous", "step_name": "continuous", "argument_1": [1, 2, 7, 10, 13, 14]}}]
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(1)_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