nri

4 ms·

1. 1.▲
   by nri 4y ago

   You are right, data/concept drift affects both approaches. What I meant to say was that retraining your model might not fix that if you baked strong assumptions into it. I edited the blog post to make it clearer.
2. 2.▲
   by nri 4y ago

   Interesting, that advice is exactly the opposite of the common wisdom. You mean overfitting as in aggressively maximizing your crossvalidation scores? How do you decide for which problems that is a good approach and for which a more conserv
3. 3.▲
   by nri 4y ago

   Glad you like the post. I strongly agree with all of your points, especially custom loss functions can be a great tool. If the problem you are trying to solve has some grounding in e.g. physics you can even go a step further and let the mod
4. 4.▲
   Some thoughts on machine learning with small data(niklasriewald.com)

   127 points by nri 4y ago|69 comments