Navigating the Toughest Part of Machine Learning: Feature Engineering

For data scientists, transforming raw data into powerful predictive models involves navigating complex, messy realities. While model selection and tuning remain indispensable skills, many experienced data professionals argue feature engineering makes or breaks a project. Here are three thorny challenges that can send even the most seasoned data teams down rabbit holes.

Challenge 1: Extracting Signals from Noise

"Our modeling is only as good as the features feeding it," says Sara Gray, principal data scientist at Semantia Inc. "Data often arrives as an undifferentiated mass before we mold it into predictive form through feature engineering."

But teasing out those signals is far from straightforward. "Domain expertise helps guide the search for meaningfulderived features," Gray explains. "But exploring combinations and transformations of raw variables requires intuition and iteration to discover what works. Too often data scientists throw models at defective features and get frustrated with poor results."

Challenge 2: The Curse of Dimensions

Mundane modeling issues can quickly escalate into nightmares due to the complexity of real-world data. "We dealt with a scenario where users could register with 5 different IDs, have multiple email addresses, and use any combo of name capitalizations," recounts Rafael Kliemann, data science manager at ProLogica.

"That exploded user dimensions to a level that impaired predictive accuracy. We had to carefully engineer aggregated user features to cut through the noise." High-dimensionality forces teams to get creative collapsing features without losing critical nuances.

Challenge 3: Changing Distributions

Finally, the underlying statistical properties of data constantly evolve. "We saw significant changes in behavior during the pandemic that degraded previously reliable models," says Rebecca Liu, senior data scientist at Osana. "Features that worked yesterday may not work tomorrow when distributions shift."

This non-stationarity requires reengineering features dynamically in response to changes in data over time. Failing to adapt leads to once-potent models decaying rapidly. "You can never retire from feature engineering," Liu stresses. "Your features need ongoing maintenance and refinement."

In summary, Feature engineering separates data science pros from amateurs

The process of molding raw data into powerful predictors requires mastering statistical complexity and business domain intricacy. There are no shortcuts when sculpting messy data into business impact. As Gray reminds, "garbage in gets you garbage out - no matter how sophisticated the ML model."

Contact Random Forest Services today to learn how our expert Data Scientists can help you with Feature Engineering.