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Exploring Supervised Learning: Regression and Classification in R
Supervised Learning In RThis document provides a practical introduction to supervised learning using R. We explore key techniques such as linear regression and logistic regression, using real datasets to demonstrate how models are trained, evaluated, and validated. Along the way, we discuss key concepts like mean squared error, overfitting, classification accuracy, and the use of confusion matrices to assess model performance.
