How to Test and Address Overfitting in Predictive Models - Examples

Overfitting is the Achilles’ heel of predictive modeling. A model that performs flawlessly on training data but fails on new data is like a student who memorizes answers without understanding concepts—it cannot generalize. In this guide, we’ll explore how to diagnose overfitting, address it using proven techniques, and ensure your model’s robustness.

1. Understanding Overfitting and the Bias-Variance Tradeoff

What is Overfitting?

Overfitting occurs when a model learns noise and idiosyncrasies in the training data instead of the underlying patterns. Key indicators:

• High training accuracy (e.g., 98%) but low validation accuracy (e.g., 70%).
• A complex model (e.g., a deep neural network with 1,000 layers) that fails on unseen data.

Bias-Variance Tradeoff

• High Bias: Oversimplified models (e.g., linear regression for nonlinear data) underfit.
• High Variance: Overly complex models (e.g., unpruned decision trees) overfit.
  The goal is to balance the two.

Read more »