Logistic regression employs a logistic function with a sigmoid (S-shaped) curve to map linear combinations of predictions and their probabilities. Sigmoid functions map any real value into ...

Figure 2: Robustness of classification to outliers depends on the type of regression used to establish thresholds. (a) The effect of outliers on classification based on linear regression.

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Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the annual income of a person based on their sex, age, State where they live and ...

Linear regression is a statistical method used to understand the relationship between an outcome variable and one or more explanatory variables. It works by fitting a regression line through the ...

Classification and regression trees are ideally suited for the analysis of complex ecological data. For such data, we require flexible and robust analytical methods, which can deal with nonlinear ...

Probit ("probability unit") regression is a classical machine learning technique that can be used for binary classification -- predicting an outcome that can only be one of two discrete values. For ...

DTSA 5020 Regression and Classification DTSA 5020 Regression and Classification Specialization: Intro to Statistical Learning Instructor: James Bird, Instructor Prior knowledge needed: Intro ...