# Bias–variance tradeoff

> In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model’s complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as the number of tunable parameters in a model increases, it becomes more flexible, […]

In statistics and machine learning, the **bias–variance tradeoff** describes the relationship between a model’s complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as the number of tunable parameters in a model increases, it becomes more flexible, and can better fit a training data set. That is, the model has lower error or lower bias. However, for more flexible models, there will tend to be greater **variance** to the model fit each time we take a set of samples to create a new training data set. It is said that there is greater variance in the model’s estimated parameters.

The **bias–variance dilemma** or **bias–variance problem** is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set:

- The *bias* error is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).

- The *variance* is an error from

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*Source: [Wikipedia](https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff)*

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- **URL:** https://wpsearchai.com/bias-variance-tradeoff/
- **Published:** 2026-01-28T18:47:58+00:00
- **Modified:** 2026-01-28T18:47:58+00:00
- **Author:** admin
- **Categories:** Machine learning