Description: Logits are the raw output values of a machine learning model before an activation function is applied. These values are generally real numbers that can be positive or negative and represent the score that a model assigns to each class in a classification problem. In the context of neural networks, logits are the result of the final layer of the network before normalization through functions like softmax or sigmoid. The softmax function, for example, converts these logits into probabilities that sum to one, allowing them to be interpreted as the likelihood of an input belonging to each class. Logits are fundamental to understanding how a model makes decisions, as they provide a clear view of the internal scores that the model calculates before making a final prediction. In many deep learning frameworks, logits are commonly used in the implementation of classification models, where the model is expected to produce an output that can be interpreted as a probability. The manipulation and analysis of logits are essential for optimizing model performance and interpreting its results.
