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Understanding Logistic Regression

Luis Valencia explains the idea of logistic regression:

Logistic Regression is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. In logistic regression, the dependent variable is a binary variable that contains data coded as 1 (yes, success, etc.) or 0 (no, failure, etc.). In other words, the logistic regression model predicts P(Y=1) as a function of X.

However, unlike ordinary linear regression, in it’s most basic form logistic regressions target value is a binary variable instead of a continuous value.

Read on to learn more about logistic regression. The point I like to make about logistic regression is that people brand new to it say it’s regression, because hey, it has “regression” in its name! People who are more familiar with it say that’s a misnomer and it’s really a classification algorithm, not a regression algorithm. But as Luis shows, people who are very familiar with it understand that it is a regression algorithm, which just happens to have nice classification properties because in many cases, elements get pushed to the edges (0 and 1).