September 2018
Intermediate to advanced
472 pages
12h 2m
English
book
Python Data Science Essentials - Third Edition
by Alberto Boschetti, Luca Massaron, Pietro Marinelli, Matteo Malosetti
Content preview from Python Data Science Essentials - Third Edition
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Random Subspaces and Random Patches
With random Subspaces, estimators differentiate because of random subsets of the features. Again, such a solution is achievable by tuning the parameters of BaggingClassifier and BaggingRegressor, by setting max_features to a number less than 1.0, representing the percentage of features to be chosen randomly for each model of the ensemble.
Instead, in Random Patches, estimators are built on subsets of both samples and features.
Let's now examine in a table the different characteristics of pasting, bagging, random subspaces, and random patches as implemented using the BaggingClassifier and BaggingRegressor in scikit-learn:
|
Ensembling |
Purpose |
Hyperparameters |
|
Pasting |
A number of models are built ... |
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