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Table Of Contents
Machine Learning For Dummies
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Ideally, in machine learning you can get the best results when your features don’t completely correlate with each other and each one has some predictive power with respect to the response you’re modeling. In reality, your features often do correlate with each other, displaying a high degree of redundancy in the information available to the dataset.
Having redundant data means that the same information is spread across multiple features. If it’s exactly the same information, it represents a perfect collinearity. If, instead, it’s not exactly the same information but varies in some way, you have collinearity between two variables or multicollinearity between more than two variables.
Redundant data is a problem that statistical theory created solutions to address long ago (because statistical computations can suffer a lot from multicollinearity). This chapter presents the topic under a statistical point of view, illustrating using the concepts...
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