AI Engineering Degree Practice Exam

The softmax function plays a critical role in machine learning, particularly in multi-class classification problems. Its primary purpose is to convert raw scores, also known as logits, into a probability distribution over multiple classes. This conversion enables the model to provide a clear interpretation of the outputs as probabilities that sum to one, making it easier to understand and compare the likelihood of different class outcomes.

Mathematically, the softmax function takes a vector of scores and exponentiates each score, normalizing these values by dividing by the sum of the exponentiated scores. This results in each output value being in the range of 0 to 1, which is indicative of a probability. Consequently, the class with the highest score after applying the softmax function can be interpreted as the predicted class, as it corresponds to the highest probability.

The mathematical formulation of the softmax function is particularly effective because it emphasizes larger scores while diminishing the impact of smaller ones. This property is essential in many applications such as neural networks for classification tasks, enabling models to make confident and interpretable predictions based on input data.

The remaining options do not align with the specific functionality of the softmax function. For example, calculating mean values is related to averaging and not indicative of generating