What is the impact of the K value on accuracy in machine learning?

What is the impact of the K value on accuracy in machine learning?

The K value, particularly in algorithms like K-Nearest Neighbors, determines how many neighbors influence the classification. A low K can make the model sensitive to noise, while a high K can smooth out the decision boundary, potentially leading to underfitting.

How do I choose the right K value for my model?

Choosing the right K value often involves experimentation. You can use techniques like cross-validation to evaluate different K values and select the one that provides the best accuracy on your validation set.

What are the consequences of choosing a K value that is too high?

If the K value is too high, the model may become too generalized, leading to underfitting. It might ignore important patterns in the data, resulting in lower accuracy.

Can the K value affect the model's performance on imbalanced datasets?

Yes, in imbalanced datasets, a high K value can lead to a bias towards the majority class, as it may dilute the influence of minority class instances.