Intermediate · Evaluation

Underfitting

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. A phenomenon where a model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both training and new data.

Technical Definition

A phenomenon where a model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both training and new data.

How it works

Underfitting occurs when a machine learning model is too simplistic or has not been trained sufficiently to capture the meaningful relationships in the training data. This results in high errors on both the training data and new, unseen data. It indicates that the model is not complex enough to learn the target function.

Related Concepts

• Overfitting — When a model learns noise and specific patterns in training data too well, causing it to perform poorly on new, unseen data.
• Bias (ethics/fairness) — Unfair prejudice or favoritism towards certain groups or things, which can influence data, system design, and user interactions.