overfitting 및 예측 data augmentation

Hi, everyone We're going to use random forest to create a prediction forest Overfitting occurs. We adjusted various parameters due to overfitting, but there is no big change.

When I looked up the reason, I checked the Internet post that it could be caused by a small number of data (1,000). As you know, in the case of image classification, data augmentation increases the amount of data by gradually transforming the shape and angle of the image.

How about increasing the amount of data in predictions like this? And we copied the entire data, and we made about three times as many data as three thousand. This prevents overfitting and increases accuracy.

But I'm not sure if this is the right way in terms of data science, so I'm writing like this.

In addition to these methods, I would like to ask you how to avoid overfitting the prediction problem or how to increase the amount of data.

Thank you!