Mastering overfitting and underfitting for optimal model performance

In ML, achieving reliable predictions is often the main goal. Overfitting and underfitting are two common obstacles to this goal. Let’s break down these concepts and outline concrete techniques to build better models.

Overfitting – when your model is too specific

Imagine your model as a student preparing for a test. Overfitting occurs when the student memorizes the practice questions perfectly but struggles to answer variations of the same questions on the actual exam. Similarly, an overfitted model gets too focused on the details of the training data, including random noise, and fails to grasp the bigger picture.

Real-world consequences

• Market research: A model obsessively tuned to existing customers’ data won’t be able to predict the behavior of new prospects with different characteristics
• Retail recommendations: A system trained exclusively on a loyal customer’...