Machine Learning Foundations

Underfitting, explained

Underfitting happens when a model is too limited or poorly trained to capture the important structure in the data.
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Part of the Semantic Notion technical glossary.

Short Definition

Underfitting happens when a model is too limited or poorly trained to capture the important structure in the data.

Intuition

The model is not flexible enough, trained enough, or informed enough to learn the real pattern.

Technical Definition

Underfitting occurs when both training and validation performance are poor because the model class, features, or optimization process is inadequate.

Example

A straight-line model may underfit a strongly nonlinear relationship.

Common Misunderstandings

Underfitting is not fixed by regularization; regularization usually makes models simpler.
Bad features can cause underfitting even with a powerful model.

Same Category

More from Machine Learning Foundations

BackpropagationBackpropagation is the algorithm used to compute gradients through a neural network efficiently.Bias-Variance TradeoffThe bias-variance tradeoff describes the balance between models that are too simple to capture structure and models that are too sensitive to training data noise.Binary ClassificationBinary classification is a prediction task where each example belongs to one of two classes.DropoutDropout is a regularization technique that randomly disables parts of a neural network during training.FeatureA feature is an input signal used by a model to make a prediction or learn a representation.Feature EngineeringFeature engineering is the process of transforming raw data into model inputs that make useful patterns easier to learn.

Related Reading

Essays that use this concept

Data Science and EvaluationOverfitting vs Underfitting vs Generalization

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