Menu
I read that the builtin ensemble methods in sklearn use decision trees as the base classifiers. Is it possible to use custom classifiers instead?
638
There are two main reasons to use an ensemble over a single model, and they are related; they are: Performance: An ensemble can make better predictions and achieve better performance than any single contributing model. Robustness: An ensemble reduces the spread or dispersion of the predictions and model performance.
True or False: Ensemble of classifiers may or may not be more accurate than any of its individual model. Usually, ensemble would improve the model, but it is not necessary. Hence, option A is correct.
This one line wrapper call converts the keras model into a scikit-learn model that can be used for Hyperparameter tuning using grid search, Random search etc but it can also be used, as you guessed it, for ensemble methods.
If you mean the random forest classes, then no, this is currently not possible. The option to allow other estimators was discussed on the scikit-learn mailing list last January, but I don't believe any actual code has come out that discussion.
84
If you use sklearn.ensemble.AdaBoostClassifier, then the answer is yes:
scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
You can assign base_estimator yourself.
24
I don't know if it helps, but you can very easily stack/combine custom classifiers using the Pipeline utilities: http://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html#pipelining
23
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
