Introduction to Multiclass Logistic Regression Classification
Explore how to use logistic regression for multiclass classification problems. Learn the differences between one-vs-rest and multinomial approaches, perform data preprocessing with the Iris dataset, and evaluate your models using confusion matrices and classification reports. Understand key concepts like predicted probabilities and ROC curves adapted for multiclass settings.
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Logistic regression is one of the most popular and widely used classification algorithms, and by default, it’s limited to a binary class classification problem. However, logistic regression can be used for multiclass classification using extensions like one-vs-rest (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 problems, such as maximum likelihood ...