Yet another ensemble method is known as stochastic gradient boosting. In this technique, many small classification or regression trees are built sequentially from residual-like measures from the previous tree. At each iteration, a tree is built from a random subsample of the data set (selected without replacement), producing an incremental improvement in the model. Ultimately, all the small trees are stacked together as a weighted sum of terms. The overall model accuracy gets progressively better with each additional term.
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