The correct answer is C. both of them.
Bias is the difference between the expected value of the model’s output and the true value. Variance is the spread of the model’s output around its expected value. Both bias and variance contribute to the generalization error of a model.
A model with high bias will tend to make the same mistake on all inputs, while a model with high variance will make different mistakes on different inputs. A model with low bias and low variance will be more accurate on unseen data.
There are a number of ways to reduce bias and variance in a model. One way is to use a larger training set. A larger training set will allow the model to learn more about the data and make more accurate predictions. Another way to reduce bias and variance is to use a more complex model. A more complex model will be able to learn more complex relationships between the inputs and outputs. However, a more complex model will also be more likely to overfit the training data and make inaccurate predictions on unseen data.
The goal of model training is to find a balance between bias and variance. A model with too much bias will not be able to learn from the data and will make inaccurate predictions. A model with too much variance will overfit the training data and will make inaccurate predictions on unseen data. The ideal model is one that has low bias and low variance.