Introduction

One of the big promises of deep learning is that it can learn directly from raw data. As a result, many assume that feature engineering is obsolete.

But is that really true?

In traditional machine learning, feature engineering was central. Practitioners spent significant time crafting new variables, transforming inputs, and injecting domain knowledge into structured features.

Deep learning, by contrast, uses models that can learn representations of the data automatically. So, can we finally skip manual feature work?

The answer, as usual, is: it depends.

What Feature Engineering Used to Look Like

In classic machine learning, feature engineering helped expose patterns to models with limited capacity.

Take a credit risk model, for example. A data scientist might:

• Count missed payments in the last six months
  
  

• Compute a loan-to-income ratio
  
  

• Add interaction terms like “high income and recent default”
  
  

These features help models like logistic regression or decision trees perform well, since they can’t learn complex relationships on their own.

What Deep Learning Brings to the Table

Deep learning models, especially neural networks and transformers, can learn representations internally from raw inputs.

Instead of explicitly creating a loan-to-income ratio, you can just provide the raw “loan amount” and “income.” The model, through layers of abstraction, may discover the importance of that ratio on its own.

This capability shines in:

• High-cardinality categories (e.g., thousands of merchant types)
  
  

• Sequences and time-based data (e.g., transaction histories)
  
  

• Unstructured data (text, images, audio)
  
  

With enough data and compute, deep learning can uncover patterns too subtle or high-dimensional for manual engineering.

So Is Feature Engineering Dead?

Not at all. It has just evolved.

Even in deep learning workflows, feature engineering still plays a vital role:

1. Efficiency
   Pre-calculated features (like ratios) can help models converge faster, especially in data-scarce environments.
   
   

2. Interpretability
   Domain-derived features like “credit utilization” remain easier to explain than black-box activations in a hidden layer.
   
   

3. Noise and Bias Handling
   Feature engineering lets you encode domain rules to reduce noise, cap outliers, or filter biased inputs.
   
   

4. Limited Data Support
   When datasets are small or noisy, engineered features can inject signal where the model would otherwise struggle.
   
   

A Balanced Approach

The difference today is why we engineer features.

• In traditional ML, features compensated for weak models
  
  

• In deep learning, they complement powerful models by injecting structure and accelerating learning
  
  

A good principle: let the model do the heavy lifting, but give it a head start where you can.

Real-World Practice

In real-world tabular deep learning projects (with tools like TabNet, FT-Transformer, or SAINT), teams often use a hybrid approach:

• Begin with raw, lightly processed features
  
  

• Add domain-informed signals (e.g., ratios, groupings)
  
  

• Use tools like SHAP, permutation importance, or attention visualizations to refine
  
  

• Iterate between feature tweaks and model improvements
  
  

This combination of human insight and model capacity tends to win in practice.

Summary

Deep learning has changed how we do feature engineering, but it hasn’t made it irrelevant.

In the modern workflow:

• Feature engineering is more selective and strategic
  
  

• It helps models learn faster, generalize better, and remain interpretable
  
  

• It bridges the gap between domain expertise and data-driven modeling

So no, feature engineering isn’t dead.
It’s just evolved.