Scala and Spark for Big Data Analytics

Multiclass classification using logistic regression

A binary logistic regression can be generalized into multinomial logistic regression to train and predict multiclass classification problems. For example, for K possible outcomes, one of the outcomes can be chosen as a pivot, and the other K−1 outcomes can be separately regressed against the pivot outcome. In spark.mllib, the first class 0 is chosen as the pivot class.

For multiclass classification problems, the algorithm will output a multinomial logistic regression model, which contains k−1binary logistic regression models regressed against the first class. Given a new data point, k−1models will be run, and the class with the largest probability will be chosen as the predicted class. In ...