How do I interpret k-fold validation results?

How do I interpret k-fold validation results?

K-fold validation helps assess the performance of a model by splitting the data into k subsets. Each subset is used for testing while the others are used for training.

What do the average scores tell me?

The average scores provide an overall estimate of the model's performance across different subsets, helping to identify its generalization ability.

What if the scores vary a lot between folds?

High variance in scores may indicate that the model is sensitive to the specific data used in each fold, suggesting potential overfitting.

How can I improve my model based on these results?

You can try tuning hyperparameters, using different algorithms, or increasing the amount of training data to improve model performance.