Machine Learning for OpenCV

Implementing a boosting regressor

Implementing a boosted regressor follows the same syntax as the boosted classifier:

In [15]: from sklearn.ensemble import GradientBoostingRegressor...      boost_reg = GradientBoostingRegressor(n_estimators=10,...                                            random_state=3)

We have seen earlier that a single decision tree can achieve 79.3% accuracy on the Boston dataset. A bagged decision tree classifier made of 10 individual regression trees achieved 82.7% accuracy. But how does a boosted regressor compare?

Let's reload the Boston dataset and split it into training and test sets. We want to make sure we use the same value for random_state so that we end up training and testing on the same subsets of the data:

In [16]: dataset = load_boston()... X = dataset.data ...