Logistic Regression


Logistic Regression is used in Supervised Learning and intended for binary classification problems to predict the probability of an instance belonging to the default class, when the Predictor variable is numeric or continuous.

Logistic Regression is a classification algorithm that uses the properties of the logistic function in which, typically, Weights are assigned to features and then fed to the logistic function, which outputs a number between 0 and 1. The decision boundary determines the classification.

Logistic Regression can be extended to data with more than two categories which is referred to as multinomial logistic regression

Logistic Regression requires proper Training dataset and properly tuned Cost function to be accurate.

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