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Regression · cho.sh

Regression

Regression

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과거 관찰로 현재의 연속적인 변수(Variable)를 추출하는 것. 예) 가격
이것이 선형 관계라면 선형 회귀 (Linear Regression)
전체 오류( $(\texttt{prediction}-\texttt{reality})^2$ )를 최소화하는 함수/변수를 찾자
입력 $x \in \mathbb{R}^D$ (Features, Covariates, Contexts, Predictors, etc)

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D

출력

y \in \mathbb{R}

(Responses, Targets, Outcomes, etc)

f: \mathbb{R}^D \to \mathbb{R}

with

f(x) = w_0 + \sum^D_{d=1} w_d x_d = w_0 + w^T x

(superscript

^T

stands for transpose)

i.e., hyperplane, parameterized by

w = [w_1 w_2 \cdots w_D]^T

(weights, weight vector, parameter vector, etc)

bias

w_0

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