What is bias-variance trade-off?

Bias points to the contrast between the qualities anticipated by the model and the genuine qualities. It is a mistake. One of the objectives of a ML calculation is to have a low bias.

Variance points to the affectability of the model to little vacillations in the preparation dataset. Another objective of a ML calculation is to have low change.

For a dataset that isn't actually direct, it is beyond the realm of imagination to expect to have both bias and change low simultaneously. A straight line model will have low change however high bias, while a high-degree polynomial will have low bias yet high difference.

There is no getting away from the connection among bias and change in AI.

Decreasing the bias increases the variance.

Decreasing the variance increases the bias.

So, there is a trade-off among 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.