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What is a Logit?

A Logit function, also known as the log-odds function, is a function that represents probability values from 0 to 1, and negative infinity to infinity. The function is an inverse to the sigmoid function that limits values between 0 and 1 across the Y-axis, rather than the X-axis. Because the Logit function exists within the domain of 0 to 1, the function is most commonly used in understanding probabilities.


How does a Logit function work?

The Logit function is represented as:
If X represents a probability, then X(1-X) is the odds, and the Logit function is the logarithm of the odds. The function plots across the graph within the domain of 0 to 1, and producing real numbers ranging from negative infinity to infinity.

Logit Functions and Machine Learning

The Logit function is used similarly to the sigmoid function in neural networks. The sigmoid, or activation, function produces a probability, whereas the Logit function takes a probability and produces a real number between negative and positive infinity. Like the sigmoid function, Logit functions are often placed as the last layer in a neural network as can simplify the data. For example, a Logit function is often used in the final layer of a neural network used in classification tasks. As the network determines probabilities for classification, the Logit function can transform those probabilities to real numbers.