Open Anupriya-Sri opened 2 years ago
"bagging methods work best with strong and complex models (e.g., fully developed decision trees), in contrast with boosting methods which usually work best with weak models (e.g., shallow decision trees)"
I'm not entirely sure which model our dataset will fall under.
Also, this will affect the interpretability of the results. I'm not sure if this is the optimal approach
We currently have less data for training Machine Learning models. I suggest that we try these approaches and compare the performance:
This thread has been opened for discussing on data augmentation techniques.