# Tag Info

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The best explanation I've heard is this: When you're doing machine learning, you assume you're trying to learn from data that follows some probabilistic distribution. This means that in any data set, because of randomness, there will be some noise: data will randomly vary. When you overfit, you end up learning from your noise, and including it in your ...

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ELI5 Version This is basically how I explained it to my 6 year old. Once there was a girl named Mel ("Get it? ML?" "Dad, you're lame."). And every day Mel played with a different friend, and every day she played it was a sunny, wonderful day. Mel played with Jordan on Monday, Lily on Tuesday, Mimi on Wednesday, Olive on Thursday .. and then on Friday Mel ...

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Overfitting implies that your learner won't generalize well. For example, consider a standard supervised learning scenario in which you try to divide points into two classes. Suppose that you are given $N$ training points. You can fit a polynomial of degree $N$ that outputs 1 on training points of the first class and -1 on training points of the second class....

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You can find the median approximately with this: Approximate Medians and other Quantiles in One Pass and With Limited Memory, to some degree of confidence. That algorithm works, but it's pretty slow. One of the authors of that paper, Gurmeet Manku, has some other publications that might be what you're looking for, also. There's a stackoverflow question ...

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First, the definition is clear: support is exactly "the fraction of transactions that contain a particular subset of items." This is a data-mining term, not a statistics term. $supp(A)$ is the fraction of transactions that contain item $A$. $supp(B)$ is the fraction of transactions that contain item $B$. $supp(A,B)$ is the fraction of transactions that ...

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