Linear regression - Revision history

Rice at 19:34, 17 May 2024

2024-05-17T19:34:03Z

← Older revision		Revision as of 19:34, 17 May 2024
Line 23:		Line 23:
	# [[Gradient Descent]]		# [[Gradient Descent]]
	# [[Newton's method]]		# [[Newton's method]]

			[[Category:Machine Learning]]

Rice: Rice moved page Linear Regression to Linear regression

2024-05-01T00:14:13Z

Rice moved page Linear Regression to Linear regression

← Older revision		Revision as of 00:14, 1 May 2024
(No difference)

Rice: /* Minimizing RSS */

2024-04-26T05:20:16Z

Minimizing RSS

← Older revision		Revision as of 05:20, 26 April 2024
Line 22:		Line 22:
	# [[Maximum likelihood estimation]]		# [[Maximum likelihood estimation]]
	# [[Gradient Descent]]		# [[Gradient Descent]]
	# [[Newton's ~~Method~~]]		# [[Newton's method]]

Rice: /* Minimizing RSS */

2024-04-26T05:19:13Z

Minimizing RSS

← Older revision		Revision as of 05:19, 26 April 2024
Line 22:		Line 22:
	# [[Maximum likelihood estimation]]		# [[Maximum likelihood estimation]]
	# [[Gradient Descent]]		# [[Gradient Descent]]
			# [[Newton's Method]]

Rice at 18:10, 17 April 2024

2024-04-17T18:10:38Z

← Older revision		Revision as of 18:10, 17 April 2024
Line 15:		Line 15:

	To train a linear regression model is to learn weight coefficients that minimize error. Error is numerically assigned a value with cost functions, usually [[RSS]].		To train a linear regression model is to learn weight coefficients that minimize error. Error is numerically assigned a value with cost functions, usually [[RSS]].

			= Minimizing RSS =
			There are several ways to minimize the RSS

			# [[Ordinary least squares]]
			# [[Maximum likelihood estimation]]
			# [[Gradient Descent]]

Rice: Created page with "'''Linear regression''' is one of the simplest used techniques for predictive modeling. It estimates a linear relationship between dependent continuous variable $y$ and attributes (aka. independent variables) $X$. $y = f(X)$ There are different types * Simple linear regression: one attribute * Multiple linear regression: multiple attributes Let the following function model the true relationship between $y$ and $X$ $\begi..."$

2024-04-15T18:35:51Z

Created page with "'''Linear regression''' is one of the simplest used techniques for predictive modeling. It estimates a linear relationship between dependent continuous variable $y$ and attributes (aka. independent variables) $X$. <math>y = f(X)</math> There are different types * Simple linear regression: one attribute * Multiple linear regression: multiple attributes Let the following function model the true relationship between $y$ and $X$ <math>\begi..."

New page

'''Linear regression''' is one of the simplest used techniques for predictive modeling. It estimates a linear relationship between dependent continuous variable $y$ and attributes (aka. independent variables) $X$.

<math>y = f(X)</math>

There are different types

* [[Simple LR|Simple linear regression]]: one attribute
* [[Multiple LR|Multiple linear regression]]: multiple attributes

Let the following function model the true relationship between $y$ and $X$

<math>\begin{aligned} y &= \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \epsilon \\ y &= w_0 + w_1 x_1 + w_2 x_2 + \ldots + \epsilon \\ y &= \sum w_j x_j + \epsilon \\ y &= \hat{y} \\ \end{aligned}</math>

where <math>\beta_i</math> is the weight coefficient of the attribute $x_i$ to be learned, and $\epsilon$ is residual error.

To train a linear regression model is to learn weight coefficients that minimize error. Error is numerically assigned a value with cost functions, usually [[RSS]].