Run
10593811

Run 10593811

Task 3954 (Supervised Classification) MagicTelescope Uploaded 07-06-2023 by george prince
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Flow

sklearn.pipeline.Pipeline(imputation=sklearn.impute._base.SimpleImputer,hot encoding=sklearn.preprocessing._encoders.OneHotEncoder,variencethreshold=sk learn.feature_selection._variance_threshold.VarianceThreshold,clf=sklearn.e nsemble._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(29)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(29)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(29)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(29)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(29)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(29)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(29)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(29)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(29)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(29)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(29)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(29)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(29)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(29)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(29)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(29)_random_state42071
sklearn.ensemble._forest.RandomForestClassifier(29)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(29)_warm_startfalse
sklearn.impute._base.SimpleImputer(44)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(44)_copytrue
sklearn.impute._base.SimpleImputer(44)_fill_valuenull
sklearn.impute._base.SimpleImputer(44)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(44)_strategy"mean"
sklearn.impute._base.SimpleImputer(44)_verbose0
sklearn.pipeline.Pipeline(imputation=sklearn.impute._base.SimpleImputer,hotencoding=sklearn.preprocessing._encoders.OneHotEncoder,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,clf=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputation=sklearn.impute._base.SimpleImputer,hotencoding=sklearn.preprocessing._encoders.OneHotEncoder,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,clf=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputation", "step_name": "imputation"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "hotencoding", "step_name": "hotencoding"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variencethreshold", "step_name": "variencethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "clf", "step_name": "clf"}}]
sklearn.pipeline.Pipeline(imputation=sklearn.impute._base.SimpleImputer,hotencoding=sklearn.preprocessing._encoders.OneHotEncoder,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,clf=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(42)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(42)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(42)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(42)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(42)_sparsetrue
sklearn.feature_selection._variance_threshold.VarianceThreshold(8)_threshold0.0

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
0.5227 ± 0.021
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5486 ± 0.0059
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0507 ± 0.0108
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1682 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3708 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.456 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6569 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Number of instances
19020
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6482 ± 0.021
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6569 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9355 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8133 ± 0.0094
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4775 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.547 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.1455 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5201 ± 0.0043
Cross-validation details (10-fold Crossvalidation)