From the course: Mistakes to Avoid in Machine Learning

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Focusing on the wrong factors

Focusing on the wrong factors

From the course: Mistakes to Avoid in Machine Learning

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Focusing on the wrong factors

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- Peter Norvig, Director of Research at Google, is often quoted as saying, "We don't have better algorithms than anyone else. We just have more data." One area where I've seen data scientists struggle is when they spin their wheels tuning and refining their machine learning model, only to hit a wall in terms of performance. If you want to avoid this outcome, I recommend revisiting the data you collected at the start of the project. There's a good chance that by incorporating more data sources into your model, you can significantly improve your result. First, I suggest you think about the data you would want, regardless of what data is actually available to you. Now map this wishlist to the data sources that exist within your business. Find and incorporate variables you don't currently have in your model. If they meet your feature selection criteria, measure their feature importance and overall impact your model's…

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