The practice of machine learning involves matching the characteristics of the input data to the biases of the available learning algorithms. Thus, before applying machine learning to real-world problems, it is important to understand the terminology that distinguishes between input datasets.

The phrase unit of observation is used to describe the smallest entity with measured properties of interest for a study. Commonly, the unit of observation is in the form of persons, objects or things, transactions, time points, geographic regions, or measurements. Sometimes, units of observation are combined to form units, such as person-years, which denote cases where the same person is tracked over multiple years, and each person-year comprises a person's data for one year.

Datasets that store the units of observation and their properties can be described as collections of:

It is easiest to understand features and examples through real-world scenarios. For instance, to build a learning algorithm to identify spam emails, the unit of observation could be email messages, examples would be specific individual messages, and the features might consist of the words used in the messages.

For a cancer detection algorithm, the unit of observation could be patients, the examples might include a random sample of cancer patients, and the features may be genomic markers from biopsied cells in addition to patient characteristics, such as weight, height, or blood pressure.

People and machines differ in the types of complexity they are suited to handle in the input data. Humans are comfortable consuming unstructured data, such as free-form text, pictures, or sound. They are also flexible handling cases in which some observations have a wealth of features, while others have very little.

On the other hand, computers generally require data to be structured, which means that each example of the phenomenon has the same features, and these features are organized in a form that a computer may understand. To use the brute force of the machine on large, unstructured datasets usually requires a transformation of the input data to a structured form.

The following spreadsheet shows data that has been gathered in matrix format. In matrix data, each row in the spreadsheet is an example and each column is a feature. Here, the rows indicate examples of automobiles for sale, while the columns record each automobile's features, such as the price, mileage, color, and transmission type. Matrix format data is by far the most common form used in machine learning. As you will see in later chapters, when forms of data are encountered in specialized applications, they are ultimately transformed into a matrix prior to machine learning.

Figure 1.9: A simple dataset in matrix format describing automobiles for sale

A dataset's features may come in various forms. If a feature represents a characteristic measured in numbers, it is unsurprisingly called numeric. Alternatively, if a feature comprises a set of categories, the feature is called categorical or nominal. A special type of categorical variable is called ordinal, which designates a nominal variable with categories falling in an ordered list. One example of an ordinal variable is clothing sizes, such as small, medium, and large; another is a measurement of customer satisfaction on a scale from "not at all happy" to "somewhat happy" to "very happy." For any given dataset, thinking about what the features represent, their types, and their units, will assist with determining an appropriate machine learning algorithm for the learning task.

Machine learning algorithms are divided into categories according to their purpose. Understanding the categories of learning algorithms is an essential first step toward using data to drive the desired action.

A predictive model is used for tasks that involve, as the name implies, the prediction of one value using other values in the dataset. The learning algorithm attempts to discover and model the relationship between the target feature (the feature being predicted) and the other features.

Despite the common use of the word "prediction" to imply forecasting, predictive models need not necessarily foresee events in the future. For instance, a predictive model could be used to predict past events, such as the date of a baby's conception using the mother's present-day hormone levels. Predictive models can also be used in real time to control traffic lights during rush hour.

Now, because predictive models are given clear instructions on what they need to learn and how they are intended to learn it, the process of training a predictive model is known as supervised learning. The supervision does not refer to human involvement, but rather to the fact that the target values provide a way for the learner to know how well it has learned the desired task. Stated more formally, given a set of data, a supervised learning algorithm attempts to optimize a function (the model) to find the combination of feature values that result in the target output.

The often-used supervised machine learning task of predicting which category an example belongs to is known as classification. It is easy to think of potential uses for a classifier. For instance, you could predict whether:

In classification, the target feature to be predicted is a categorical feature known as the class, which is divided into categories called levels. A class can have two or more levels, and the levels may or may not be ordinal. Classification is so widely used in machine learning that there are many types of classification algorithms, with strengths and weaknesses suited for different types of input data. We will see examples of these later in this chapter and throughout this book.

Supervised learners can also be used to predict numeric data, such as income, laboratory values, test scores, or counts of items. To predict such numeric values, a common form of numeric prediction fits linear regression models to the input data. Although regression is not the only method for numeric prediction, it is by far the most widely used. Regression methods are widely used for forecasting, as they quantify in exact terms the association between the inputs and the target, including both the magnitude and uncertainty of the relationship.

A descriptive model is used for tasks that would benefit from the insight gained from summarizing data in new and interesting ways. As opposed to predictive models that predict a target of interest, in a descriptive model, no single feature is more important than any other. In fact, because there is no target to learn, the process of training a descriptive model is called unsupervised learning. Although it can be more difficult to think of applications for descriptive models—after all, what good is a learner that isn't learning anything in particular—they are used quite regularly for data mining.

For example, the descriptive modeling task called pattern discovery is used to identify useful associations within data. Pattern discovery is the goal of market basket analysis, which is applied to retailers' transactional purchase data. Here, retailers hope to identify items that are frequently purchased together, such that the learned information can be used to refine marketing tactics. For instance, if a retailer learns that swimming trunks are commonly purchased at the same time as sunscreen, the retailer might reposition the items more closely in the store or run a promotion to "up-sell" customers on associated items.

The descriptive modeling task of dividing a dataset into homogeneous groups is called clustering. This is sometimes used for segmentation analysis, which identifies groups of individuals with similar behavior or demographic information in order to target them with advertising campaigns based on their shared characteristics. With this approach, the machine identifies the clusters, but human intervention is required to interpret them. For example, given a grocery store's five customer clusters, the marketing team will need to understand the differences among the groups in order to create a promotion that best suits each group. Despite this human effort, this is still less work than creating a unique appeal for each customer.

Lastly, a class of machine learning algorithms known as meta-learners is not tied to a specific learning task, but rather is focused on learning how to learn more effectively. A meta-learning algorithm uses the result of past learning to inform additional learning.

This encompasses learning algorithms that learn to work together in teams called ensembles, as well as algorithms that seem to evolve over time in a process called reinforcement learning. Meta-learning can be beneficial for very challenging problems or when a predictive algorithm's performance needs to be as accurate as possible.

Some of the most exciting work being done in the field of machine learning today is in the domain of meta-learning. For instance, adversarial learning involves learning about a model's weaknesses in order to strengthen its future performance or harden it against malicious attack. There is also heavy investment in research and development efforts to make bigger and faster ensembles, which can model massive datasets using high-performance computers or cloud-computing environments.

The following table lists the general types of machine learning algorithms covered in this book. Although this covers only a fraction of the entire set of machine learning algorithms, learning these methods will provide a sufficient foundation for making sense of any other methods you may encounter in the future.

To begin applying machine learning to a real-world project, you will need to determine which of the four learning tasks your project represents: classification, numeric prediction, pattern detection, or clustering. The task will drive the choice of algorithm. For instance, if you are undertaking pattern detection, you are likely to employ association rules. Similarly, a clustering problem will likely utilize the k-means algorithm, and numeric prediction will utilize regression analysis or regression trees.

For classification, more thought is needed to match a learning problem to an appropriate classifier. In these cases, it is helpful to consider the various distinctions among the algorithms—distinctions that will only be apparent by studying each of the classifiers in depth. For instance, within classification problems, decision trees result in models that are readily understood, while the models of neural networks are notoriously difficult to interpret. If you were designing a credit scoring model, this could be an important distinction because the law often requires that the applicant must be notified about the reasons he or she was rejected for the loan. Even if the neural network is better at predicting loan defaults, if its predictions cannot be explained, then it is useless for this application.

To assist with algorithm selection, in every chapter the key strengths and weaknesses of each learning algorithm are listed. Although you will sometimes find that these characteristics exclude certain models from consideration, in many cases the choice of algorithm is arbitrary. When this is true, feel free to use whichever algorithm you are most comfortable with. Other times, when predictive accuracy is the primary goal, you may need to test several models and choose the one that fits best, or use a meta-learning algorithm that combines several different learners to utilize the strengths of each.