Classification

Why classification?

There are many times where you have to make a decision. Either A or B. Either Emily or Tom (It’s 2021. It’s ok to be bisexual). This either … or … theme is the heart of classification problems.

How does classification work?

Let’s say we are classifying an image. The image is said to be either a cat or a dog. But which one? If we are given the information that there are only black cats and white dogs in the image, we would probably apply a heuristic saying that if the object in the image is bright, then it’s likely a dog. If it’s dark then it’s a cat. In machine words, if the image’s average is bright (assuming that the cat/dog will occupy the majority of the image), then it’s a dog. Or else it’s a cat.

What we did above is basically mapping from an image to 2 labels, cat and dog. This is how all models solving classification problem work. If you can find a model that maps the input image (or sound or anything) to your desired label, then it’s a good classifier.