The Softmax function is a mathematical function that converts a vector of real numbers into a probability distribution. It is widely used in machine learning, particularly in the output layer of neural networks for multi-class classification tasks. By applying the Softmax function, each element of the input vector is transformed into a value between 0 and 1, representing the probability of each class, with the sum of all probabilities equal to 1.
Properties:
- Probability Distribution: The output values are in the range (0, 1) and sum to 1, making them interpretable as probabilities.
- Sensitivity to Input Magnitudes: Larger input values correspond to higher probabilities, and the function amplifies the differences between input values.
Applications:
- Multi-Class Classification: In neural networks, the Softmax function is commonly used in the output layer to predict the probability distribution over multiple classes.
- Decision Making: It helps in making decisions by selecting the class with the highest probability.