This is a great overview of bias and variance.
The online machine learning course given by Andrew Ng in 2011 (available here among many other places, including YouTube) is highly recommended in its entirety, but I just wanted to highlight a specific part of it, namely the “Practical advice part”, which touches on things that are not always included in machine learning and data mining courses, like “Deciding what do to do next” (the title of this lecture) or “debugging a learning algorithm” (the title of the first slide in that talk).
His advice here focuses on the concepts of the bias and variance in statistical learning. I had been vaguely aware of the concepts of “bias and variance tradeoff” and “bias/variance decomposition” for a long time, but I had always viewed those as theoretical concepts that were mostly helpful for thinking about the properties of learning algorithms; I hadn’t thought that much about connecting them to the…
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