The quality of predictive output relies on the quality of input — if you put good in, you’ll get good out. That’s why proper data preparation is such a critical success factor for achieving optimal machine learning results. The iterative process of preparing data for automated machine learning is both an art and a science.
In this White Paper from Jen Underwood of Impact Analytix, she will walk you through the entire machine learning lifecycle and the steps you should take to collect, prepare and understand your data.
In this report, you'll learn:
- How to avoid overfitting and underfitting
- The key to detecting leakage in order to find and reduce errors
- How to improve data quality and engineer features