regression

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- regression is fitting line onto data points
- machine learning

Knowing:

data points $\vec{x} = (x_{1}, x_{2}, . . ., x_{n}) \in X$
$\vec{x} = (x_{1} . . . x_{n})$ satisfy $f (\vec{x}, P) = f (x_{1}, x_{2}, . . ., x_{n}, P) = 0$ with a set of parameters $P$

Find:

$P$ (which due to noise in $X$ is unlikely) or
$P^{'}$ that optimize/minimize some function $f^{'} (\vec{x}, P^{'})$ (usually error of $f (\vec{x}, P^{'})$ over $X$ )
regression is fitting line onto data points
linear regression
polynomial regression
gnuplot line fitting

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regression is fitting line onto data points

The model is known in regression analysis (e.g. y = kx + b), and with regression algorithms, parameters (e.g. k and b) would be approximated. As result, a real line, instead of a parameterized (e.g. with k = 3 and b = -1) can be drawn, fitting the data point, providing clear correlation insights

machine learning

Mainly, Agent with a learning function, which generally, takes in data that encodes its experience, and modify its behaviour according to that data.(which makes data science its sister discipline)

regression

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regression is fitting line onto data points

machine learning