Machine Learning for OpenCV 4 - Second Edition

Implementing a bagging classifier

We can, for instance, build an ensemble from a collection of 10 k-NN classifiers as follows:

In [1]: from sklearn.ensemble import BaggingClassifier...     from sklearn.neighbors import KNeighborsClassifier...     bag_knn = BaggingClassifier(KNeighborsClassifier(),...                                 n_estimators=10)

The BaggingClassifier class provides a number of options to customize the ensemble:

• n_estimators: As shown in the preceding code, this specifies the number of base estimators in the ensemble.
• max_samples: This denotes the number (or fraction) of samples to draw from the dataset to train each base estimator. We can set bootstrap=True to sample with replacement (effectively implementing bagging), or we can set bootstrap=False to implement ...