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Logistic regression is a supervised machine learning algorithm widely used for binary classification tasks, such as identifying whether an email is spam or not and diagnosing diseases by assessing the presence or absence of specific conditions based on patient test results. This approach utilizes the logistic (or sigmoid) function to transform ...
Linear regression is also a type of machine learning algorithm, more specifically a supervised algorithm, that learns from the labelled datasets and maps the data points to the most optimized linear functions that can be used for prediction on new datasets. [3]
Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals.
In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning. [1] [2] Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its leaves to provide a ...
The basic form of a linear predictor function () for data point i (consisting of p explanatory variables), for i = 1, ..., n, is = + + +,where , for k = 1, ..., p, is the value of the k-th explanatory variable for data point i, and , …, are the coefficients (regression coefficients, weights, etc.) indicating the relative effect of a particular explanatory variable on the outcome.
This solution closely resembles that of standard linear regression, with an extra term . If the assumptions of OLS regression hold, the solution w = ( X T X ) − 1 X T y {\displaystyle w=\left(X^{\mathsf {T}}X\right)^{-1}X^{\mathsf {T}}y} , with λ = 0 {\displaystyle \lambda =0} , is an unbiased estimator, and is the minimum-variance linear ...
IRLS is used to find the maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers in an otherwise normally-distributed data set, for example, by minimizing the least absolute errors rather than the least square errors.
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 error-free independent variables (often called regressors, predictors, covariates, explanatory ...