Notation

Let the data be of the form , where:

• , (number of classes = 2 because it's a binary classification)

• is 'n' dimensional, that is, (refers to the preceding diagram)

• The number of training examples is m. Thus the training set looks as follows:

  • .

  • m = size of training dataset.

  • And, since , where, each .

  • Therefore, is a matrix of size n * m, that is, number of features * number of training examples.

  • , a vector of m outputs, where, each .

  • Parameters : Weights , and bias , where and is a scalar value.