Concept of Logistic Regression

I 've one doubt about Logistic Regression Topic

logistic regression expression tells
y^ = sigmoid function( WT*X + b)

here Why we need to include WT and b and how the calculation is done from the feature vector?

In the vectorized form:

  • '‘w’ is the vector of the weights.
  • ‘X’ is the matrix of examples.
  • The ‘*’ means you’re computing the dot product.

For each example, there is a vector of features. Which one is the rows and which is the columns depends on how the data set was built.

Whether you need the ‘T’ (which stands for “transposition”) depends on the shapes of w and X.