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The Intuition Behind Principal Component Analysis

Holger von Jouanne-Diedrich gives us an intuition behind how principal component analysis (PCA) works: Principal component analysis (PCA) is a dimension-reduction method that can be used to reduce a large set of (often correlated) variables into a smaller set of (uncorrelated) variables, called principal components, which still contain most of the information.PCA is a concept […]

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Gradient Boosting And XGBoost

Shirin Glander has another English-language transcript from a German video, this time covering gradient boosting techniques: Let’s look at how Gradient Boosting works. Most of the magic is described in the name: “Gradient” plus “Boosting”. Boosting builds models from individual so called “weak learners” in an iterative way. In the Random Forests part, I had already discussed the […]

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