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  "body": "\n## Summary\n\nKernel regression estimates the regression function $m(x) = E[Y \\mid X = x]$ without assuming a parametric form for it.\nThe estimate at $x$ is a weighted average of the observed responses, with weights given by a kernel that favours observations with $X_i$ near $x$.\nIts standard form is the Nadaraya–Watson estimator, a regression analogue of [[kernel density estimation|ZV65XMJ9A4]].\n\n## Explanations\n\n### L1 human\n\nTo guess the typical value of $Y$ at a point $x$, look at the data points whose $X$ is close to $x$ and average their $Y$ values.\nCloser points get more weight, and the bandwidth decides how far \"close\" reaches.\nFor example, to estimate the typical weight of people of height $170$ cm, one averages the weights of people of nearby heights.\nThe result is a smooth curve through the cloud of data points.\n",
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