Simply put, supervised learning finds associations between features of a dataset and a target variable. For example, supervised learning models might try to find the association between a person's health features (heart rate, obesity level, and so on) and that person's risk of having a heart attack (the target variable).

These associations allow supervised models to make predictions based on past examples. This is often the first thing that comes to people's minds when they hear the phrase, machine learning, but it in no way does it encompass the realm of machine learning. Supervised machine learning models are often called predictive analytics models, named for their ability to predict the future based on the past.

Supervised machine learning requires a certain type of data called labeled data. This means that we must teach our model by giving it historical examples that are labeled with the correct answer. Recall the facial recognition example. That is a supervised learning model because we are training our model with the previous pictures labeled as either face or not face, and then asking the model to predict whether or not a new picture has a face in it.

Specifically, supervised learning works using parts of the data to predict another part. First, we must separate data into two parts, as follows:

Supervised learning attempts to find a relationship between the predictors and the response in order to make a prediction. The idea is that, in the future, a data observation will present itself and we will only know the predictors. The model will then have to use the predictors to make an accurate prediction of the response value.

Suppose we wish to predict whether someone will have a heart attack within a year. To predict this, we are given that person's cholesterol level, blood pressure, height, their smoking habits, and perhaps more. From this data, we must ascertain the likelihood of a heart attack. Suppose, to make this prediction, we look at previous patients and their medical history. As these are previous patients, we know not only their predictors (cholesterol, blood pressure, and so on), but we also know if they actually had a heart attack (because it already happened!).

This is a supervised machine learning problem because we are doing the following:

The hope here is that a patient will walk in tomorrow and our model will be able to identify whether or not the patient is at risk for a heart attack based on her/his conditions (just like a doctor would!).

As the model sees more and more labeled data, it adjusts itself in order to match the correct labels given to us. We can use different metrics (explained later in this chapter) to pinpoint exactly how well our supervised machine learning model is doing and how it can better adjust itself.

One of the biggest drawbacks of supervised machine learning is that we need this labeled data, which can be very difficult to get a hold of. Suppose we wish to predict heart attacks; we might need thousands of patients along with all of their medical information and years' worth of follow-up records for each person, which could be a nightmare to obtain.

In short, supervised models use historical labeled data in order to make predictions about the future. Some possible applications for supervised learning include the following:

Note how each of the preceding examples uses the word prediction, which makes sense seeing how I emphasized supervised learning's ability to make predictions about the future. Predictions, however, are not where the story ends.

Here is a visualization of how supervised models use labeled data to fit themselves and prepare themselves to make predictions:

Note how the supervised model learns from a bunch of training data and then, when it is ready, it looks at unseen cases and outputs a prediction.

Types of supervised learning

There are, in general, two types of supervised learning models: regression and classification. The difference between the two is quite simple and lies in the response variable.

Regression

Regression models attempt to predict a continuous response. This means that the response can take on a range of infinite values. Consider the following examples:

• Dollar amounts
  • Salary
  • Budget
• Temperature
• Time
  • Generally recorded in seconds or minutes

Classification

Classification attempts to predict a categorical response, which means that the response only has a finite amount of choices. Here are some examples:

• Cancer grade (1, 2, 3, 4, 5)
• True/false questions, such as the following examples:
  • "Will this person have a heart attack within a year?"
  • "Will you get this job?"
• Given a photo of a face, who does this face belong to? (facial recognition)
• Predict the year someone was born:
  • Note that there are many possible answers (over 100) but still finitely many more

Example – regression

The following graphs show a relationship between three categorical variables (age, year they were born, and education level) and a person's wage:

Regression examples (source: https://lagunita.stanford.edu/c4x/HumanitiesScience/StatLearning/asset/introduction.pdf)

Note that, even though each predictor is categorical, this example is regressive because the y axis, our dependent variable, our response, is continuous.

Our earlier heart attack example is classification because the response was: will this person have a heart attack within a year? This has only two possible answers: Yes or No.

The second type of machine learning does not deal with predictions but has a much more open objective. Unsupervised learning takes in a set of predictors and utilizes relationships between the predictors in order to accomplish tasks such as the following:

The first element on this list is called dimension reduction and the second is called clustering. Both of these are examples of unsupervised learning because they do not attempt to find a relationship between predictors and a specific response and therefore are not used to make predictions of any kind. Unsupervised models, instead, are utilized to find organizations and representations of the data that were previously unknown.

The following screenshot is a representation of a cluster analysis:

Example of cluster analysis

The model will recognize that each uniquely colored cluster of observations is similar to another but different from the other clusters.

A big advantage for unsupervised learning is that it does not require labeled data, which means that it is much easier to get data that complies with unsupervised learning models. Of course, a drawback to this is that we lose all predictive power because the response variable holds the information to make predictions and, without it, our model will be hopeless in making any sort of predictions.

A big drawback is that it is difficult to see how well we are doing. In a regression or classification problem, we can easily tell how well our models are predicting by comparing our models' answers to the actual answers. For example, if our supervised model predicts rain and it is sunny outside, the model was incorrect. If our supervised model predicts the price will go up by 1 dollar and it goes up by 99 cents, our model was very close! In supervised modeling, this concept is foreign because we have no answer to compare our models to. Unsupervised models are merely suggesting differences and similarities, which then require a human's interpretation:

An overview of unsupervised models

In short, the main goal of unsupervised models is to find similarities and differences between data observations. We will discuss unsupervised models in depth in later chapters.

Reinforcement learning

In reinforcement learning, algorithms get to choose an action in an environment and then are rewarded (positively or negatively) for choosing this action. The algorithm then adjusts itself and modifies its strategy in order to accomplish some goal, which is usually to get more rewards.

This type of machine learning is very popular in AI-assisted gameplay as agents (the AI) are allowed to explore a virtual world and collect rewards and learn the best navigation techniques. This model is also popular in robotics, especially in the field of self-automated machinery, including cars:

Self-driving cars (image source: https://www.quora.com/How-do-Googles-self-driving-cars-work)

Self-driving cars read in sensor input, act accordingly, and are then rewarded for taking a certain action. The car then adjusts its behavior to collect more rewards. It can be thought that reinforcement is similar to supervised learning in that the agent is learning from its past actions to make better moves in the future; however, the main difference lies in the reward. The reward does not have to be tied in any way to a correct or incorrect decision. The reward simply encourages (or discourages) different actions.

Reinforcement learning is the least explored of the three types of machine learning and therefore is not explored in great length in this text. The remainder of this chapter will focus on supervised and unsupervised learning.

Overview of the types of machine learning

Of the three types of machine learning—supervised, unsupervised, and reinforcement learning—we can imagine the world of machine learning as something like this:

Each of the three types of machine learning has its benefits and its drawbacks, as listed:

• Supervised machine learning: This exploits relationships between predictors and response variables to make predictions of future data observations. The pros are as follows:
  • It can make future predictions
  • It can quantify relationships between predictors and response variables
  • It can show us how variables affect each other and how much

  The cons are as follows:

  • It requires labeled data (which can be difficult to get)
• Unsupervised machine learning: This finds similarities and differences between data points. The pros are as follows:
  • It can find groups of data points that behave similarly that a human would never have noted.
  • It can be a preprocessing step for supervised learning.Think of clustering a bunch of data points and then using these clusters as the response.
  • It can use unlabeled data, which is much easier to find.

  The cons are as follows:

  • It has zero predictive power
  • It can be hard to determine if we are on the right track
  • It relies much more on human interpretation
• Reinforcement learning: This is reward-based learning that encourages agents to take particular actions in their environments. The pros are as follows:
  • Very complicated rewards systems create very complicated AI systems
  • It can learn in almost any environment, including our own Earth

  The cons are as follows:

  • The agent is erratic at first and makes many terrible choices before realizing that these choices have negative rewards

  For example, a car might crash into a wall and not know that that is not okay until the environment negatively rewards it

  • It can take a while before the agent avoids decisions altogether
  • The agent might play it safe and only choose one action and be "too afraid" to try anything else for fear of being punished