February 2016
Beginner to intermediate
312 pages
7h 23m
English
Content preview from Regression Analysis with PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Gradient Boosting Regressor with LAD
More than a new technique, this is an ensemble of technologies already seen in this book, with a new loss function, the Least Absolute Deviations (LAD). With respect to the least square function, seen in the previous chapter, with LAD the L1 norm of the error is computed.
Regressor learners based on LAD are typically robust but unstable, because of the multiple minima of the loss function (leading therefore to multiple best solutions). Alone, this loss function seems to bear little value, but paired with gradient boosting, it creates a very stable regressor, due to the fact that boosting overcomes LAD regression limitations. With the code, this is very simple to achieve:
In: from sklearn.ensemble import GradientBoostingRegressor ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.