What happens with insufficient data in machine learning?

What happens if I don't have enough data for my machine learning model?

Insufficient data can lead to overfitting, where the model learns noise instead of the underlying pattern.

Can you explain overfitting a bit more?

Sure! Overfitting occurs when a model is too complex and captures noise in the training data, resulting in poor performance on new data.

How can I mitigate the effects of having insufficient data?

You can use techniques like data augmentation, transfer learning, or gathering more data to improve your model's performance.

Is there a minimum amount of data needed for machine learning?

There's no strict minimum, but generally, more data leads to better models. It depends on the complexity of the problem.