Gradient Boosted Machines

Gradient boosting machines (GBMs) are advanced ensemble techniques that sequentially build and combine weak prediction models, typically decision trees, to produce a stronger predictive performance. Unlike random forests, GBMs construct trees one at a time, each aiming to minimize errors from previous models. Another way to think about this is while random forests build a collection of decision trees in parallel, GBMs build them sequentially. This is where the term boosting comes from: we try to boost the predictive performance of each successive tree. This iterative approach can significantly enhance accuracy, making GBMs highly effective for various machine learning tasks. This recipe will introduce GBMs as another ensemble approach for ML modeling.

Getting ready

We will use scikit-learn to illustrate how to create a gradient boosting classifier.

1. Load the libraries:

   import numpy as np
   import pandas as pd
   from sklearn.datasets import load_iris
   from sklearn...