What is Softmax? And why the softmax function is suitable for multiple classification but not regression?

Softmax is a mathematical function that is often used in machine learning and deep learning models to convert a set of input values into a set of output probabilities that sum to one. The softmax function is applied to a vector of real numbers, typically the output of a neural network, and produces a probability distribution over the different possible classes or categories.

 

 

The softmax function is suitable for multiple classification tasks because it produces a probability distribution over the different possible classes or categories. In other words, given a set of input values, the softmax function outputs a set of probabilities that represent the likelihood of the input belonging to each possible class. This makes it well-suited for classification tasks where the goal is to assign a categorical label to each input instance.

 

However, the softmax function is not suitable for regression tasks, where the goal is to predict a continuous output value. In regression tasks, we want to predict a numerical value, not a probability distribution over different categories. The softmax function outputs probabilities that sum to 1, which is not appropriate for regression tasks where the output can take any numerical value.

 

For regression tasks, other activation functions such as the identity function or the ReLU (rectified linear unit) function are often used in the output layer of neural networks. These activation functions produce a continuous output that can take any value within a specified range, making them well-suited for regression tasks.