How do I choose the right K value for KNN?

How do I choose the right K value for KNN?

Choosing the right K value is crucial for KNN performance. A smaller K can be noisy and lead to overfitting, while a larger K can smooth out the decision boundary but may underfit. A common approach is to use cross-validation.

What is cross-validation?

Cross-validation is a technique used to assess how the results of a statistical analysis will generalize to an independent dataset. It involves partitioning the data into subsets, training the model on some subsets, and validating it on others.

How do I implement cross-validation for KNN?

You can implement cross-validation for KNN using libraries like scikit-learn in Python. Use the KFold or StratifiedKFold classes to split your dataset and evaluate the model's performance for different K values.

What K values should I try?

A good starting point is to try odd values like 1, 3, 5, 7, and 9. This helps avoid ties in classification problems. You can then analyze the performance metrics to find the optimal K.