Supervised Learning: Bayesian Inference
Tags: machinelearning, supervisedlearning
Published: 2019-02-23 09:02:44

---
title: "Supervised Learning: Bayesian Inference"
published: true
description: Or, the unreasonable effectiveness of dumb rules
tags: Machine Learning, Supervised Learning
---

*This is the 11th in a series of class notes as I go through the [Georgia Tech/Udacity Machine Learning course](https://www.udacity.com/course/machine-learning--ud262). The class textbook is [Machine Learning by Tom Mitchell](https://www.cs.ubbcluj.ro/~gabis/ml/ml-books/McGrawHill%20-%20Machine%20Learning%20-Tom%20Mitchell.pdf).*

> This chapter builds on the previous one on Bayesian Learning, and is skimpy because we skipped a lot of basic probability content.  

> This is also the end of a miniseries on Supervised Learning, [the 1st of 3 sub disciplines within Machine Learning](https://dev.to/swyx/machine-learning-an-overview-216n).


## What is Bayesian Inference?

Representing probabilities, and calculating them. For example, what is the probability of X happening given Y? But on steroids.

## Bayesian Networks

> Also known as Belief Networks or Graphical Models.

The idea is to represent conditional relationships as nodes on a directed acyclic graph. Edges are therefore dependencies to be considered in your model.

![https://cdn-images-1.medium.com/max/1600/1*9OsQV0PqM2juaOtGqoRISw.jpeg](https://cdn-images-1.medium.com/max/1600/1*9OsQV0PqM2juaOtGqoRISw.jpeg)

This can look like a neural net. Dependencies can skip levels and therefore the network's connections can grow exponentially with every variable:

![http://www.pr-owl.org/images/bn_wisepilot.jpg](http://www.pr-owl.org/images/bn_wisepilot.jpg)

This makes inference in complex Bayesian networks hard to do.

One thing to note about dependencies is that they don't necessarily reflect cause-and-effect relationships, just ones that are conditionally dependent on the other. Perhaps more importantly, *lack* of dependencies are very good, because they reflect [conditional independence](https://en.wikipedia.org/wiki/Conditional_independence).

The acyclic nature of belief networks mean you can do a [topological sort](https://en.wikipedia.org/wiki/Topological_sorting) of the nodes to order your calculations.

## Inferencing Rules

Three handy rules we use in Bayesian Inference are:

- Marginalization (intuitively, adding up the conditional probabilities)

![https://image.slidesharecdn.com/02introtoprobabilitylukas-121203104049-phpapp02/95/introduction-to-probability-7-638.jpg?cb=1354531567](https://image.slidesharecdn.com/02introtoprobabilitylukas-121203104049-phpapp02/95/introduction-to-probability-7-638.jpg?cb=1354531567)

- Chain rule (joint distribution of two attributes is an attribute 1's probability, times probability of other attributes given attribute 1)
- Bayes rule (re-expressing conditional probabilities)

![https://slideplayer.com/slide/5071518/16/images/12/Doug+Downey+%28adapted+from+Bryan+Pardo%2C+Northwestern+University%29.jpg](https://slideplayer.com/slide/5071518/16/images/12/Doug+Downey+%28adapted+from+Bryan+Pardo%2C+Northwestern+University%29.jpg)

## Naive Bayes
   
Naive Bayes is a special case of Bayesian Networks, that assumes ALL attributes are conditionally independent of each other, i.e. a very very simple network with just one layer:

![https://www.researchgate.net/profile/Julian_Ortiz4/publication/226687183/figure/fig1/AS:393643681697806@1470863376002/Bayesian-network-representing-the-Naive-Bayes-classifier-with-attributes-B-1-B-2.png](https://www.researchgate.net/profile/Julian_Ortiz4/publication/226687183/figure/fig1/AS:393643681697806@1470863376002/Bayesian-network-representing-the-Naive-Bayes-classifier-with-attributes-B-1-B-2.png)

If you take the top node as a class, and take all the child nodes as attributes, you can reverse the direction of the Bayes Net and infer the class from the attributes, arriving at a Naive Bayes Classifier:

![https://shirishkadam.files.wordpress.com/2016/04/selection_005.png?w=760](https://shirishkadam.files.wordpress.com/2016/04/selection_005.png?w=760)

## Naive Bayes: Pros and Cons

There are a number of benefits to this approach:

- It makes inference (normally a np-hard problem) cheap
- It is linear, not exponential, in number of attributes
- It is easy to estimate these parameters with labeled data (by simple count)
- connects inference and classification - instead of only generating probabilities of attributes, you can flip it and generate classification
- Empirically it is very successful - [Google has a patented version](https://patents.google.com/patent/US8364766) of Naive Bayes used for spam filtering.

However:

- its "naïveté" comes from assuming that there are no interrelationships between any of the attributes, which is hard to believe
    - the answer is: yes, its inaccurate, but [like we said in the last lesson](https://dev.to/swyx/supervised-learning-bayesian-learning-403l) we don't care about getting the exact right hypothesis to estimate probabilities, we just care about getting the right answer in classification. So you just need to be directionally correct.
- Relying on an empirical count means missing attributes have zero estimated probability
    - this exposes you to inductive bias and overfitting to your data
    - yes, this is a problem, so in practice people "smooth probabilities" by initializing them with a small nonzero weight.

## Next in our series

Further notes on this topic:

- [Wikipedia on Bayesian Inference](https://en.wikipedia.org/wiki/Bayesian_inference)

Hopefully that was a good introduction to Bayesian Inference. I am planning more primers and would love your feedback and questions on:



- [Overview](https://dev.to/swyx/machine-learning-an-overview-216n)
- Supervised Learning
    - [Decision Trees](https://dev.to/swyx/machine-learning-classification-learning--decision-trees-1mbh)
    - [Regression](https://dev.to/swyx/supervised-learning-regression-4d17)
    - [Neural Networks](https://dev.to/swyx/supervised-learning-neural-networks-mpo)
    - [Instance Based Learning (K Nearest Neighbors)](https://dev.to/swyx/supervised-learning-instance-based-learning-and-k-nearest-neighbors-kge)
    - [Ensemble Learning (AdaBoost)](https://dev.to/swyx/supervised-learning-ensemble-learning-lim)
    - [Kernel Methods & SVMs](https://dev.to/swyx/supervised-learning-support-vector-machines-3mgk)
    - [Computational Learning Theory](https://dev.to/swyx/supervised-learning-computational-learning-theory-160h)
    - [VC Dimensions](https://dev.to/swyx/supervised-learning-vc-dimensions-10b)
    - [Bayesian Learning](https://dev.to/swyx/supervised-learning-bayesian-learning-403l)
    - [Bayesian Inference](https://dev.to/swyx/supervised-learning-bayesian-inference-4l72)
- Unsupervised Learning
    - [Randomized Optimization](https://dev.to/swyx/unsupervised-learning-randomized-optimization-4c1i)
    - [Information Theory](https://dev.to/swyx/unsupervised-learning-information-theory-recap-4iem)
    - Clustering - week of Feb 25
    - Feature Selection - week of Mar 4
    - Feature Transformation - week of Mar 11
- Reinforcement Learning
    - Markov Decision Processes - week of Mar 25
    - "True" RL - week of Apr 1
    - Game Theory - week of Apr 15