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e. In statistics, linear regression is a statistical model which estimates the linear relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables ). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear ...
Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. [1]
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 modeled as an n th degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E ( y | x ).
t. e. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables ...
Linear trend estimation is a statistical technique used to analyze data patterns. Data patterns, or trends, occur when the information gathered, tends to increase or decrease over time, or is influenced by changes in an external factor. Linear trend estimation essentially creates a straight line on a graph of data that models the general ...
In computational learning theory, probably approximately correct ( PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant. [ 1] In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions.
Multivariate adaptive regression spline. In statistics, multivariate adaptive regression splines ( MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. [ 1] It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between ...
Linear predictor function. In statistics and in machine learning, a linear predictor function is a linear function ( linear combination) of a set of coefficients and explanatory variables ( independent variables ), whose value is used to predict the outcome of a dependent variable. [1] This sort of function usually comes in linear regression ...