Softmax Function
In the context of Data Science and Artificial Intelligence, the Softmax Function is a type of squashing function that is used in various multiclass classification methods. It is an activation function that turns numbers, also known as logits, into probabilities that sum to one. The function outputs a vector that represents the probability distributions of a list of potential outcomes. It is most commonly used in applied machine learning, where it acts as an activation function in a neural network model. The Softmax function is beneficial in situations where we need probabilities for multiple classes in a classification problem, as it highlights the most probable classes while also maintaining a part of the less probable ones. It is an essential part of the logistic function, which is used to calculate the probabilities in logistic regression models. The Softmax function is also used in reinforcement learning, where it can be used to select actions.
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