What is K-Fold Cross-Validation?
An example of an AI chatbot conversation about K-Fold Cross-Validation in machine learning.
What is K-Fold Cross-Validation?
K-Fold Cross-Validation is a technique used to assess the performance of a machine learning model by dividing the dataset into 'K' subsets.
How does it work?
In K-Fold Cross-Validation, the dataset is split into K equal parts. The model is trained on K-1 parts and tested on the remaining part. This process is repeated K times.
What are the benefits of using K-Fold?
It helps in reducing overfitting, provides a better estimate of model performance, and utilizes the entire dataset for both training and testing.
Are there any drawbacks?
Yes, it can be computationally expensive, especially with large datasets, and the choice of K can affect the results.
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