Gradient boosting machines (GBMs) are currently very popular and so it's a good idea for machine learning practitioners to understand how GBMs work. The problem is that understanding all of the mathematical machinery is tricky and, unfortunately, these details are needed to tune the hyper-parameters. (Tuning the hyper-parameters is required to get a decent GBM model unlike, say, Random Forests.) Our goal in this article is to explain the intuition behind gradient boosting, provide visualizations for model construction, explain the mathematics as simply as possible, and answer thorny questions such as why GBM is performing “gradient descent in function space.” We've split the discussion into three morsels and a FAQ for easier digestion. Written by Terence Parr and Jeremy Howard.
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Gradient boosting machines (GBMs) are currently very popular and so it's a good idea for machine learning practitioners to understand how GBMs work. The problem is that understanding all of the mathematical machinery is tricky and, unfortunately, these details are needed to tune the hyper-parameters. (Tuning the hyper-parameters is required to get a decent GBM model unlike, say, Random Forests.) Our goal in this article is to explain the intuition behind gradient boosting, provide visualizations for model construction, explain the mathematics as simply as possible, and answer thorny questions such as why GBM is performing “gradient descent in function space.” We've split the discussion into three morsels and a FAQ for easier digestion. Written by Terence Parr and Jeremy Howard.