# Limitations

* Our model is a simplification of reality.
* Simplification is based on assumptions (model bias).
* Assumptions fail in certain situations.

## Bias and Variance

<figure><img src="/files/3DU9u6QKWIYMHzL0IcW5" alt=""><figcaption></figcaption></figure>

Bias measures the model's ability to fit the training data accurately, while variance measures its ability to generalize to new, unseen data.

**Bias**, also known as underfitting, is a measure of how well a model can fit the training data. A high bias indicates that the model is too simplistic and cannot capture the underlying patterns in the data. It leads to poor performance on the training data.

**Variance**, also known as overfitting, is a measure of how well a model can generalize to new, unseen data. High variance suggests that the model is overly complex and fits the noise in the training data, making it perform poorly on unseen data.


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