I'm working on a predictive maintenance project with a dataset of about 5,000 rows in a CSV format. I’m tempted to use a Deep Neural Network because of the hype, but I’ve heard that Random Forests or XGBoost might actually perform better for smaller, structured data. At what point does the complexity of a Deep Learning model actually become an advantage over standard ML?
3 answers
Deep Learning usually requires a massive amount of data to generalize. For 5k rows, you'll likely just be training a very expensive noise-memorizer.
For a dataset of 5,000 rows, Deep Learning is likely overkill and might even perform worse due to overfitting. Traditional Machine Learning algorithms like Gradient Boosted Trees (XGBoost or LightGBM) are specifically optimized for tabular data and handle missing values and feature scaling much better than a standard Multi-Layer Perceptron. Deep Learning truly shines when you have hundreds of thousands of samples or when the data is unstructured, like images or audio. In your case, stick to "shallow" ML; you'll get better interpretability and faster training times without the need for expensive GPU resources.
Are you planning to perform manual feature engineering, or were you hoping the Deep Learning model would extract those patterns automatically?
David, that's exactly the trade-off. With only 5,000 rows, a Neural Network won't have enough signal to "self-discover" features effectively. I've found that in smaller industrial datasets, the domain knowledge you bake into manual features for an XGBoost model is worth more than any automated architecture. I'd suggest starting with a simple Random Forest to establish a baseline before even touching Keras or PyTorch.
Exactly, Linda. The "curse of dimensionality" hits hard here. Without at least 50k-100k points, the weights in a deep network rarely converge to a meaningful global minimum.