Feature engineering is an important part of leveraging big datasets. Even with the right technical skills and domain knowledge, it can still be a time consuming
Your data is teeming with potential insights, ready to be teased out by predictive models. But doing that isn’t only about knowing what questions to ask
So, you’ve built a dataset you’re happy with, and your machine learning (ML) model ready to start making predictions left and right. Easy as pie, right?
Feature selection algorithms are increasingly growing in significance. In this article, we will cover (and compare) two popular feature selection methodologies - Filter and Wrapper.
Better features, better data
(or – “how I f***ed up my text classifier while thinking it’s performing well")
