The differences and applications of Supervised and Unsupervised Machine Learning.
Machine learning is one of the buzziest terms thrown around in technology these days. Combine machine learning with big data in a Google search and you’ve got yourself an unmanageable amount of information to digest. In an (possibly ironic) effort to help navigate this sea of information, this post is meant to be an introduction and simplification of some common machine learning terminology and types with some resources to dive deeper.
At the highest level, there are two different types of machine learning - supervised and unsupervised. Supervised means that we have historical information in order to learn from and make future decisions; unsupervised means that we have no previous information, but might be attempting to group things together or do some other type of pattern or outlier recognition.
In each of these subsets there are many methodologies and motivations; I’ll explain how they work and give a simple example or two.
Supervised machine learning is nothing more than using historical information (read: data) in order to predict a future event or explain a behavior using algorithms. I know - this is vague - but humans use these algorithms based on previous learning everyday in their lives to predict things.
A very simple example: if it is sunny outside when we wake up, it is perfectly reasonable to assume that it will not rain that day. Why do we make this prediction? Because over time, we’ve learned that on sunny days it typically does not rain. We don’t know for sure that today it won’t rain but we’re willing to make decisions based on our prediction that it won’t rain.
Computers do this exact same thing in order to make predictions. The real gains come from Supervised Machine Learning when you have lots of accurate historical data. In the example above, we can’t be 100% sure that it won’t rain because we’ve also woken up on a few sunny mornings in which we’ve driven home after work in a monsoon - adding more and more data for your supervised machine learning algorithm to learn from also allows it to make concessions for these other possible outcomes.
Supervised Machine Learning can be used to classify (usually binary or yes/no outcomes but can be broader - is a person going to default on their loan? will they get divorced?) or predict a value (how much money will you make next year? what will the stock price be tomorrow?). Some popular supervised machine learning methods are regression (linear, which can predict a continuous value, or logistic, which can predict a binary value), decision trees, k-nearest neighbors, and naive Bayes.
My favorite of these methods is decision trees. A decision tree is used to classify your data. Once the data is classified, the average is taken of each terminal node; this value is then applied to any future data that fits this classification.
The decision tree above shows that if you were a female and in first or second class, there was a high likelihood you survived. If you were a male in second class who was younger than 12 years old, you also had a high likelihood of surviving. This tree could be used to predict the potential outcomes of future sinking ships (morbid… I know).
Unsupervised machine learning is the other side of this coin. In this case, we do not necessarily want to make a prediction. Instead, this type of machine learning is used to find similarities and patterns in the information to cluster or group.
An example of this: Consider a situation where you are looking at a group of people and you want to group similar people together. You don’t know anything about these people other than what you can see in their physical appearance. You might end up grouping the tallest people together and the shortest people together. You could do this same thing by weight instead… or hair length… or eye color… or use all of these attributes at the same time! It’s natural in this example to see how “close” people are to one another based on different attributes.
What these type of algorithms do is evaluate the “distances” of one piece of information from another piece. In a machine learning setting you look for similarities and “closeness” in the data and group accordingly.
This could allow the administrators of a mobile application to see the different types of users of their app in order to treat each group with different rules and policies. They could cluster samples of users together and analyze each cluster to see if there are opportunities for targeted improvements.
The most popular of these unsupervised machine learning methods is called k-means clustering. In k-means clustering, the goal is to partition your data into k clusters (where k is how many clusters you want - 1, 2,…, 10, etc.). To begin this algorithm, k means (or cluster centers) are randomly chosen. Each data point in the sample is clustered to the closest mean; the center (or centroid, to use the technical term) of each cluster is calculated and that becomes the new mean. This process is repeated until the mean of each cluster is optimized.
The important part to note is that the output of k-means is clustered data that is “learned” without any input from a human. Similar methods are used in Natural Language Processing (NLP) in order to do Topic Modeling.
There are an uncountable amount resources out there to dive deeper into this topic. Here are a few that I’ve used or found along my Data Science journey.
UPDATE: I’ve written a whole post on this. You can find it here
- O’Reilly has a ton of great books that focus on various areas of machine learning.
- edX and coursera have a TON of self-paced and instructor-led learning courses in machine learning. There is a specific series of courses offered by Columbia University that look particularly applicable.
- If you are interested in learning machine learning and already have a familiarity with R and Statistics, DataCamp has a nice, free program. If you are new to R, they have a free program for that, too.
- There are also many, many blogs out there to read about how people are using data science and machine learning.