Model overfitting vs underfitting

Post date: Mar 11, 2017 4:7:0 PM

Final thoughts/conclusions on this classical issue:

Poor performance on train data => low model complexity/underfit => need to increase model complexity by

+ increase model(feature) dim

+ reduce regularization

Divergence between train and test performance => high model complexity/overfit => need to reduce model complexity by

+ decrease model(feature) dim

+ increase regularization

If the performance on both train and test data is still inadequate => not enough data

+ get more data

+ iterate with more epoches/passes on the existing dataset

Amazon puts it best [1]!

[1] http://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html