Hi @RohitKamalV

In the context of the logistic equation or sigmoid function, the expression “w.x+b” represents the linear part of the equation, where “w” and “b” are the weights and bias, respectively, of the input variable “x”.

Please let me know if this answer your question

Welcome to the community, @RohitKamalV!

In addition to @pastorsoto’s great answer:

It describes the exponent e.g. used in a logistic regression task; or let’s say:

- x: the
**input of a sigmoid function**, - b: the bias
- w: the weight(s)

where w and b are the model parameters that are learned within model training.

But dependent on your model you can also interpret this term as the cell body of a neuron within a larger neuron network (e.g. at a output layer of a binary classification problem):

Here also a visual interpretation possibility with a neuron where you can think of **f as a sigmoid function**, representing a nonlinear activation function of this layer:

Source: Choice of activation function - #8 by Christian_Simonis

Best regards

Christian

It doesn’t have a specific name.