Question

Variance and bias of a model move in opposite directions. When you increase the bias in a model, the variance decreases, and vice versa. This is known as the bias-variance trade-off. You need to strike a balance between bias and variance to build a model that generalizes well to new data.

Answer 1

The bias-variance trade-off refers to the relationship between the bias and variance of a model, and we need to strike a balance between the two to build a model that generalizes well to new data.

Step-by-step explanation:

The bias-variance trade-off refers to the relationship between the bias and variance of a model. Bias measures how far off the predictions of a model are from the true values, while variance measures how much the predictions of a model vary for different training samples.

When a model has high bias, it means that it is too simple and cannot capture the complexity of the data. On the other hand, when a model has high variance, it means that it is too flexible and overfits the training data.

To strike a balance between bias and variance, we need to find the optimal complexity of the model that minimizes both bias and variance, leading to better generalization to new data.