[sytelus]

The hardest part in machine learning is not training the model but debugging the model. How do you improve precision/recall after the first cut? Do you need more training data? Is some of your training data bad? Is it properly distributed? Does your feature have bug? Are you missing features to cover some cases? Is your feature selection effective? Did you tuned parameters carefully?

All these scenarios are difficult to debug because it's "statistical debugging". There are no breakpoints to put or watch windows to look at. There is no stack trace and there are no exceptions. Any Joe can train a model given training data, it takes fair bit of genius to debug these issues and push model performance to next level. Unfortunately all these new and old "frameworks" almost completely ignore this debugging part. I think the first framework that has great debugging tools will revolutionize ML like Borland revolutionized programming with its visual IDEs.