Why are nonlinear activation functions preferable?1 pointNonlinear activation functions are NOT preferable to linear ones, as they lose information in systems with high variance.Nonlinear activation functions increase the functional capacity of the neural network by allowing the representation of nonlinear relationships between features in input.Nonlinear activation functions are preferable because they have been used historically.Nonlinear activation functions are preferable because they are used in generalized linear models in statistics.

Nonlinear activation functions are preferable primarily because they increase the functional capacity of the neural network. This is achieved by allowing the representation of nonlinear relationships between features in the input. This means that they can model complex patterns and behaviors that linear activation functions may not be able to.

The statement that nonlinear activation functions are not preferable because they lose information in systems with high variance is not generally true. While it's true that some information might be lost due to the nonlinearity, this is often a trade-off for the ability to model complex behaviors.

The fact that nonlinear activation functions have been used historically or that they are used in generalized linear models in statistics does not inherently make them preferable. What makes them preferable is their ability to increase the complexity and adaptability of the neural network models.