How do I tune the K value in my machine learning model?

How do I tune the K value in my machine learning model?

Tuning the K value involves selecting the optimal number of neighbors for algorithms like K-NN. You can use techniques like cross-validation.

What is cross-validation and how does it help in tuning?

Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset. It helps in tuning by providing a more reliable estimate of model performance.

Are there any specific methods for choosing the K value?

Yes, methods like the elbow method, grid search, and random search can help you find the best K value.

What is the elbow method?

The elbow method involves plotting the error rate against different K values and looking for a point where the rate of decrease sharply changes, resembling an 'elbow'.