I’m a Data Analyst trying to level up my skills. I keep hearing that Deep Learning is the "future," but I'm wondering if it’s overkill for standard tabular data like customer churn prediction. Does a Neural Network really provide a better accuracy boost than a Random Forest or XGBoost model for structured data, or is it only meant for complex things like computer vision?
3 answers
For structured/tabular data (like your churn prediction), traditional Machine Learning models like XGBoost or LightGBM often outperform Deep Learning. Neural Networks generally require massive amounts of data and significant computational power to "beat" a well-tuned Random Forest. Deep Learning shines in "Unstructured" data—images, audio, and natural language—where it can automatically perform feature extraction. In traditional ML, you have to manually engineer your features. If you're working with Excel-style data, stick to ML; if you're building a voice assistant, go Deep.
Sabrina, do you think the rise of "Tabular Transformers" is starting to close the gap where Deep Learning might finally become the standard for all data types?
I always start with a Baseline ML model. If that doesn't hit the accuracy targets, only then do I consider the complexity of a Deep Learning architecture.
That's the best way to do it, Victor. Start simple. Most people jump to the most complex solution first and end up wasting weeks on a model that won't deploy easily.
Franklin, while Tabular Transformers are exciting, they still require a lot more "hyperparameter tuning" and hardware than a simple Gradient Boosted Tree. For 90% of business use cases, the interpretability of a traditional ML model—knowing why a customer is likely to churn—is more valuable than a 0.5% increase in accuracy from a complex, "black-box" Deep Learning model.