Bias refers to the difference between the values predicted by the model and the real values. It is an error. One of the goals of an ML algorithm is to have a low bias.

Variance refers to the sensitivity of the model to small fluctuations in the training data set. Another goal of an ML algorithm is to have low variance.

For a data set that is not exactly linear, it is not possible to have both bias and variance low at the same time. A straight line model will have low variance but high bias, whereas a high-degree polynomial will have low bias but high variance.

There is no escaping the relationship between bias and variance in machine learning.

- Decreasing the bias increases the variance.
- Decreasing the variance increases the bias.

So, there is a trade-off between the two; the ML specialist has to decide, based on the assigned problem, how much bias and variance can be tolerated. Based on this, the final model is built.

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