Why giving your algorithm ALL THE FEATURES does not always work

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We'd like to think of ML algorithms as smart, and sophisticated, learning machines. But they can be fooled by the different types of noise present in your data. Training an algorithm on a large set of variables, hoping that your model will separate signal from noise, is not always the right approach. We'll discuss different ways to do feature selection, and discuss open-source implementations. Slides: https://lnkd.in/gyJw8gp

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