Summary
Explore how to apply logistic regression for multiclass classification using OVR and multinomial approaches. Understand the challenges of imbalanced datasets and learn strategies like SMOTE, oversampling, and undersampling to handle them. Discover how to evaluate model performance accurately, avoiding the accuracy paradox, and ensuring robust predictions on unseen data.
In this chapter, we learned about the following concepts.
Multiclass logistic regression classification
Logistic regression is one of the most popular and widely used classification algorithms, and by default, it is limited to a binary class classification problem. However, logistic regression can be used for multiclass classification using extensions like OVR and multinomial.
In OVR, the problem is first transformed into multiple binary classification problems, and under the hood, separate binary classifiers are trained for all classes.
In multinomial, the solvers learn an accurate multinomial logistic regression model. In this case, the probability estimates should be better calibrated than OVR. The cross-entropy error/loss function supports multiclass classification ...