Run
10457870

Run 10457870

Task 9900 (Supervised Classification) abalone Uploaded 19-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._pca.PCA ,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGrad ientBoostingClassifier)(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._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization1.9891012587630113e-05
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.3094392438001447
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins228
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth2
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter668
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes4080
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf475
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change14
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"precision_macro"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.05961849100557029
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.11616509055414032
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.decomposition._pca.PCA(1)_copyfalse
sklearn.decomposition._pca.PCA(1)_iterated_power"auto"
sklearn.decomposition._pca.PCA(1)_n_components1
sklearn.decomposition._pca.PCA(1)_random_state42
sklearn.decomposition._pca.PCA(1)_svd_solver"arpack"
sklearn.decomposition._pca.PCA(1)_tol0.2921067154721685
sklearn.decomposition._pca.PCA(1)_whitenfalse
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._pca.PCA,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._pca.PCA,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._pca.PCA,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(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.

18 Evaluation measures

Area under ROC curve
0.7591 ± 0.0129
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5482 ± 0.0175
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3322 ± 0.0253
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.291 ± 0.0128
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3451 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4441 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5566 ± 0.0174
Cross-validation details (10-fold Crossvalidation)
Number of instances
4177
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5484 ± 0.0252
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5566 ± 0.0174
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.584 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7771 ± 0.0128
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4712 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4141 ± 0.0048
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8787 ± 0.0101
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5508 ± 0.0164
Cross-validation details (10-fold Crossvalidation)