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In logistic regression, the dependent variable is binary or dichotomous, i.e. it only contains data coded as 1 (TRUE, success, pregnant, etc.) or 0 (FALSE, failure, non-pregnant, etc.).. The goal of ...

Learn how to use logistic regression to predict the probability of a binary outcome based on explanatory variables, and understand the assumptions and interpretations of the model.

In the demo problem, the two predictor variables, Age and Edu, are numeric. Logistic regression can handle categorical predictor variables, too. Similarly, the values to predict "red", "blue" were ...

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 ...

Decision Trees Regression: Decision tree regression uses a tree-like model to predict continuous numerical values and is ideal for use over logistic regression when categorical outcomes are not ...