<ul><li>Generalization is the model’s ability to apply what it has learned during training to new data.</li><li>Underfitting occurs when the model is too simplistic to capture the underlying patterns in the data.</li><li>Overfitting happens when the model learns not only the underlying patterns but also the noise in the training data.</li><li>The key to good generalization in kNN is finding the optimal value of k that balances bias and variance.</li></ul>

Understanding Generalization, Underfitting, and Overfitting in k-Nearest Neighbors (kNN)

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