What activation function should I use for a specific regression problem?

Answer: For regression problems, commonly used activation functions include linear activation, as it directly outputs the weighted sum of inputs without introducing non-linearity.

In the realm of regression problems, selecting an appropriate activation function is pivotal for the successful training of a neural network. Unlike classification problems where non-linear activation functions like sigmoid or SoftMax are commonly used to introduce non-linearity and capture complex patterns, regression tasks focus on predicting continuous values.

For regression, the linear activation function is a natural choice. The linear activation function, often denoted as f(x) = x, simply outputs the weighted sum of its input without applying any non-linear transformation. This function is well-suited for regression because it allows the model to learn and predict continuous values without distorting the relationship between inputs and outputs.

When using the linear activation function, the neural network essentially becomes a linear combination of its inputs, akin to a linear regression model. The absence of non-linear transformations ensures that the network can represent a wide range of numerical values without constraining the output to a specific range or introducing unnecessary complexities.

Conclusion:

In summary, for regression problems, it is advisable to employ the linear activation function due to its simplicity and compatibility with the nature of predicting continuous values. This choice facilitates straightforward learning of the linear relationships between input features and the target variable, providing a well-suited foundation for regression tasks.