Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the first part in a series that walks through it one step at a time. This video focuses on the main ideas behind using Gradient Boost to predict a continuous value, like someone's weight. We call this, "using Gradient Boost for Regression". In the next video, we'll work through the math to prove that Gradient Boost for Regression really is this simple. In part 3, we'll walk though how Gradient Boost classifies samples into two different categories, and in part 4, we'll go through the math again, this time focusing on classification.
This StatQuest assumes that you already understand....
Decision Trees: [ Ссылка ]
Regression Trees: [ Ссылка ]
AdaBoost: [ Ссылка ]
...and the tradeoff between Bias and Variance that plagues Machine Learning: [ Ссылка ]
For a complete index of all the StatQuest videos, check out:
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This StatQuest is based on the following sources:
A 1999 manuscript by Jerome Friedman that introduced Stochastic Gradient Boost: [ Ссылка ]
The Wikipedia article on Gradient Boosting: [ Ссылка ]
The scikit-learn implementation of Gradient Boosting: [ Ссылка ]
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0:00 Awesome song and introduction
2:58 Gradient Boost compared to AdaBoost
5:50 Building the first tree to predict weight
10:37 Building the second tree to predict weight
13:28 Building additional trees to predict weight
13:50 Prediction with Gradient Boost
14:28 Summary of concepts and main ideas
#statquest #gradientboost
