A lot of people seem to confuse the two terms in the context of machine learning. This post will try to clarify what we mean by the two, where each one is useful, and how they are applied. I personally understood it when I had a class called Intelligent Data and Probabilistic Inference (by Duncan Gillies) in my Master’s degree at Imperial College London some years back. Here, I will present a couple of examples in order to intuitively understand the difference.

Inference:

You observe the grass in your backyard. It is wet. You observe the sky. It is cloudy. You _infer _it has rained. You then open the TV and watch the channel weather. It is cloudy but no rain for a couple of days. You remember you had a timer for the sprinkler a few hours ago. You _infer _that this is the cause of the grass being wet.

(The creepy example) Imagine you are staring at an object in the evening that is a bit far away in a corner. Getting closer… you observe that the object is staring back at you. You infer that is an animal. You are brave enough and you are getting closer. You can now see the eyes, the fur, the legs, and other characteristics of the animal. You _infer that it is a cat. _A simple procedure for your brain, right? It feels trivial to you and probably stupid to even discuss it. You can, of course, recognize a cat, but, in fact, this is a form of inference.

Say the cat has some features like eyes, fur, shape, etc. As you get closer to it, you assign different values to these variables. For example, initially, the eyes variable was set to 0, as you couldn’t see them. As you move closer you are more certain of what you observe. Your brain takes these observations and converts them in the probability that the object is a cat. Say we have a catness variable that represents the possibility of the object being a cat. Initially, this variable could be near zero. Catness is increased as you move closer to the object. Inference takes place and updates your belief about the catness of the object.

#inference #predictions #data-science #machine-learning