How does the quantity of data affect overfitting in machine learning models?

How does the quantity of data affect overfitting in machine learning models?

The quantity of data plays a crucial role in overfitting. More data generally helps models generalize better, reducing overfitting.

Can you explain why more data helps in reducing overfitting?

Certainly! More data provides a broader representation of the underlying patterns, allowing the model to learn more effectively and avoid memorizing noise.

Is there a point where adding more data stops being beneficial?

Yes, after a certain point, the benefits of additional data may diminish, especially if the data is redundant or not diverse enough.

What strategies can I use to prevent overfitting besides increasing data?

You can use techniques like regularization, dropout, and cross-validation to help prevent overfitting, even with limited data.