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ML Terminology
In mathematics, extrapolation is the process of estimating, beyond the original observation range, the value of a variable on the basis of its relationship with another variable.
외삽법 (Extrapolation)이란 이전의 경험에 비추어, 보다 과학적인 맥락에서는 이전의 실험으로부터 얻은 데이터들에 비추어, 아직 경험/실험하지 못한 경우를 예측해보는 기법이다. 어디까지나 추측이므로 엄밀한 추론이 아니다
In statistics, linear regression is a linear approach to modelling the relationship between a scalar response and one or more explanatory variables. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression.
통계학에서, 선형 회귀(線型回歸, 영어: linear regression)는 종속 변수 y와 한 개 이상의 독립 변수 (또는 설명 변수) X와의 선형 상관 관계를 모델링하는 회귀분석 기법이다.
In statistics, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably".
과적합(overfitting)은 기계 학습(machine learning)에서 학습 데이타를 과하게 학습(overfitting)하는 것을 뜻한다. 일반적으로 학습 데이타는 실제 데이타의 부분집합이며, 실제 데이타를 모두 수집하는 것은 불가능하다.
In mathematics, a set A is a subset of a set B, or equivalently B is a superset of A, if A is "contained" inside B, that is, all elements of A are also elements of B. A and B may coincide. The relationship of one set being a subset of another is called inclusion or sometimes containment.
In mathematics, a coefficient is a multiplicative factor in some term of a polynomial, a series, or any expression; it is usually a number, but may be any expression. In the latter case, the variables appearing in the coefficients are often called parameters, and must be clearly distinguished from the other variables.
계수(係數)의 계(係)는 매다, 묶다. 관계하다라는 뜻이야. 묶여있는 수인거지. 어디에? 바로 변수에 묶여있는 수라는 뜻. 즉 효과를 내기위해 함께 작동하는 것을 말해. 늘 변수와 붙어서 변수와 함께 관계를 맺고 있는 놈이 바로 계수야.
In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems, regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting.
In mathematics, a polynomial is an expression consisting of variables and coefficients, that involves only the operations of addition, subtraction, multiplication, and non-negative integer exponents of variables. An example of a polynomial of a single indeterminate, x, is x² − 4x + 7.
문자의 거듭제곱의 상수 배 여럿의 합을 표현하는 수식이다. 예를 들어, x2 - 2x + 3, 4x3, 5xy + 6은 모두 다항식이다.
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x.
A polynomial function is a function such as a quadratic, a cubic, a quartic, and so on, involving only non-negative integer powers of x. We can give a general defintion of a polynomial, and define its degree.
함수값이 정의역의 원소에 대해 다항식 형태로 정의되는 함수를 의미 함수f(x) 가 x 에 대한 다항식이기에 '다항함수'라고 하는 것이다. 수 y = f (x) 에서f(x) 가 x 에 대한 다항식일 때 이 함수가 다항함수이다.
Ridge Regression is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value.
In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
그 함수의 값이 정의된 집합
함수의 값들이 속하는 집합
A hypothesis is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories
Bias is disproportionate weight in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair. Biases can be learned by watching cultural contexts.
Variance is a measurement of the spread between numbers in a data set. The variance measures how far each number in the set is from the mean.