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The power of error analysis, tree models for search relevancy, what ChatGPT means for data scientists - Sergey Feldman - The Data Scientist Show #059
The Data Scientist Show
How Do You Know if You're Not Overfitting the Model?
There will never be a certain way to make all subset of data perfectly predicted. How do you decide, how do you prioritize? I have two main techniques that I use to make sure I didn't overfit this training set for too long. Having more training data that you can pull out towards the end of the process is really useful. It's very useful because later when you're going to do retraining for a production model, which you often have to do,. it might break because it was over fit to the training data.
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