Machine Learning for OpenCV 4 - Second Edition

Implementing a boosting classifier

For example, we can build a boosting classifier from a collection of 10 decision trees as follows:

In [11]: from sklearn.ensemble import GradientBoostingClassifier...      boost_class = GradientBoostingClassifier(n_estimators=10,...                                               random_state=3)

These classifiers support both binary and multiclass classification.

Similar to the BaggingClassifier class, the GradientBoostingClassifier class provides a number of options to customize the ensemble:

• n_estimators: This denotes the number of base estimators in the ensemble. A large number of estimators typically results in better performance.
• loss: This denotes the loss function (or cost function) to be optimized. Setting loss='deviance' implements logistic regression ...