You’ve spent weeks (or months) building a new machine learning model, and you think you’ve cracked it. But, when the rubber meets the road, the answers you get seem disconnected from reality. The problem could be that although your model seems great on paper, the data you used to train it is riddled with errors and inconsistencies.
So, what can you do to make it better? The good thing is that making your training data better is much easier than you think, and you can use several easy strategies for quick wins. By focusing on the areas where you can make small but noticeable improvements, you can turn a standard dataset into a training set that will boost your machine learning results.
This whitepaper discusses:
Quick strategies and methods to fine-tune and manipulate your existing data to get better testing results from it (with code snippets!)
The reasons you need to pay closer attention to your data collection processes for better datasets
Ways you can acquire more and better data to improve your testing capabilities