Binary Classification
In a binary classification problem, the result is a discrete value output.
For example - account hacked (1) or compromised (0)
- a tumor malign (1) or benign (0)
Example: Cat vs Non-Cat
The goal is to train a classifier that the input is an image represented by a feature vector, , and predicts
whether the corresponding label is 1 or 0. In this case, whether this is a cat image (1) or a non-cat image
(0).
An image is store in the computer in three separate matrices corresponding to the Red, Green, and Blue
color channels of the image. The three matrices have the same size as the image, for example, the
resolution of the cat image is 64 pixels X 64 pixels, the three matrices (RGB) are 64 X 64 each.
The value in a cell represents the pixel intensity which will be used to create a feature vector of n-
dimension. In pattern recognition and machine learning, a feature vector represents an object, in this
case, a cat or no cat.
To create a feature vector, , the pixel intensity values will be “unroll” or “reshape” for each color. The
dimension of the input feature vector is
.
64
64
red
green
blue
