How to Improve Machine Learning: Tricks and Tips for Feature Engineering

Editor’s Note: This is the first in a four-part series on improving analytics output with feature engineering. Visit Data Informed all this week for the subsequent entries in the series. Jacob Joseph, Senior Data Scientist, CleverTap Predictive modeling is a formula that transforms a list of input fields or variables into some output of interest. Feature engineering is simply a thoughtful creation of new input fields from existing input fields, either in an automated fashion or manually, with valuable inputs from domain expertise, logical reasoning, or intuition. The new input fields could result in better inferences and insights from data and exponentially increase the performance of predictive models. Feature engineering is one of the most important parts of the data preparation process, where deriving new and meaningful variables takes place. Feature engineering enhances and…


Link to Full Article: How to Improve Machine Learning: Tricks and Tips for Feature Engineering