39 min read

If science fiction stories are to be believed, the invention of artificial intelligence inevitably leads to apocalyptic wars between machines and their makers. In the early stages, computers are taught to play simple games of tic-tac-toe and chess. Later, machines are given control of traffic lights and communications, followed by military drones and missiles. The machines’ evolution takes an ominous turn once the computers become sentient and learn how to teach themselves. Having no more need for human programmers, humankind is then “deleted.”

Thankfully, at the time of this writing, machines still require user input.

Though your impressions of machine learning may be colored by these mass media depictions, today’s algorithms are too application-specific to pose any danger of becoming self-aware. The goal of today’s machine learning is not to create an artificial brain, but rather to assist us in making sense of the world’s massive data stores.

Putting popular misconceptions aside, by the end of this article, you will gain a more nuanced understanding of machine learning. You also will be introduced to the fundamental concepts that define and differentiate the most commonly used machine learning approaches.

(For more resources related to this topic, see here.)

You will learn:

  • The origins and practical applications of machine learning
  • How computers turn data into knowledge and action
  • How to match a machine learning algorithm to your data

The field of machine learning provides a set of algorithms that transform data into actionable knowledge. Keep reading to see how easy it is to use R to start applying machine learning to real-world problems.

The origins of machine learning

Since birth, we are inundated with data. Our body’s sensors—the eyes, ears, nose, tongue, and nerves—are continually assailed with raw data that our brain translates into sights, sounds, smells, tastes, and textures. Using language, we are able to share these experiences with others.

From the advent of written language, human observations have been recorded. Hunters monitored the movement of animal herds, early astronomers recorded the alignment of planets and stars, and cities recorded tax payments, births, and deaths. Today, such observations, and many more, are increasingly automated and recorded systematically in the ever-growing computerized databases.

The invention of electronic sensors has additionally contributed to an explosion in the volume and richness of recorded data. Specialized sensors see, hear, smell, taste, and feel. These sensors process the data far differently than a human being would. Unlike a human’s limited and subjective attention, an electronic sensor never takes a break and never lets its judgment skew its perception.

Although sensors are not clouded by subjectivity, they do not necessarily report a single, definitive depiction of reality. Some have an inherent measurement error, due to hardware limitations. Others are limited by their scope. A black and white photograph provides a different depiction of its subject than one shot in color. Similarly, a microscope provides a far different depiction of reality than a telescope.

Between databases and sensors, many aspects of our lives are recorded. Governments, businesses, and individuals are recording and reporting information, from the monumental to the mundane. Weather sensors record temperature and pressure data, surveillance cameras watch sidewalks and subway tunnels, and all manner of electronic behaviors are monitored: transactions, communications, friendships, and many others.

This deluge of data has led some to state that we have entered an era of Big Data, but this may be a bit of a misnomer. Human beings have always been surrounded by large amounts of data. What makes the current era unique is that we have vast amounts of recorded data, much of which can be directly accessed by computers. Larger and more interesting data sets are increasingly accessible at the tips of our fingers, only a web search away. This wealth of information has the potential to inform action, given a systematic way of making sense from it all.

The field of study interested in the development of computer algorithms to transform data into intelligent action is known as machine learning. This field originated in an environment where available data, statistical methods, and computing power rapidly and simultaneously evolved. Growth in data necessitated additional computing power, which in turn spurred the development of statistical methods to analyze large datasets. This created a cycle of advancement, allowing even larger and more interesting data to be collected.


A closely related sibling of machine learning, data mining, is concerned with the generation of novel insights from large databases. As the implies, data mining involves a systematic hunt for nuggets of actionable intelligence. Although there is some disagreement over how widely machine learning and data mining overlap, a potential point of distinction is that machine learning focuses on teaching computers how to use data to solve a problem, while data mining focuses on teaching computers to identify patterns that humans then use to solve a problem.

Virtually all data mining involves the use of machine learning, but not all machine learning involves data mining. For example, you might apply machine learning to data mine automobile traffic data for patterns related to accident rates; on the other hand, if the computer is learning how to drive the car itself, this is purely machine learning without data mining.

The phrase “data mining” is also sometimes used as a pejorative to describe the deceptive practice of cherry-picking data to support a theory.

Uses and abuses of machine learning

Most people have heard of the chess-playing computer Deep Blue—the first to win a game against a world champion—or Watson, the computer that defeated two human opponents on the television trivia game show Jeopardy. Based on these stunning accomplishments, some have speculated that computer intelligence will replace humans in many information technology occupations, just as machines replaced humans in the fields, and robots replaced humans on the assembly line.

The truth is that even as machines reach such impressive milestones, they are still relatively limited in their ability to thoroughly understand a problem. They are pure intellectual horsepower without direction. A computer may be more capable than a human of finding subtle patterns in large databases, but it still needs a human to motivate the analysis and turn the result into meaningful action.

Machines are not good at asking questions, or even knowing what questions to ask. They are much better at answering them, provided the question is stated in a way the computer can comprehend. Present-day machine learning algorithms partner with people much like a bloodhound partners with its trainer; the dog’s sense of smell may be many times stronger than its master’s, but without being carefully directed, the hound may end up chasing its tail.


To better understand the real-world applications of machine learning, we’ll now consider some cases where it has been used successfully, some places where it still has room for improvement, and some situations where it may do more harm than good.

Machine learning successes

Machine learning is most successful when it augments rather than replaces the specialized knowledge of a subject-matter expert. It works with medical doctors at the forefront of the fight to eradicate cancer, assists engineers and programmers with our efforts to create smarter homes and automobiles, and helps social scientists build knowledge of how societies function. Toward these ends, it is employed in countless businesses, scientific laboratories, hospitals, and governmental organizations. Any organization that generates or aggregates data likely employs at least one machine learning algorithm to help make sense of it.

Though it is impossible to list every use case of machine learning, a survey of recent success stories includes several prominent applications:

  • Identification of unwanted spam messages in e-mail
  • Segmentation of customer behavior for targeted advertising
  • Forecasts of weather behavior and long-term climate changes
  • Reduction of fraudulent credit card transactions
  • Actuarial estimates of financial damage of storms and natural disasters
  • Prediction of popular election outcomes
  • Development of algorithms for auto-piloting drones and self-driving cars
  • Optimization of energy use in homes and office buildings
  • Projection of areas where criminal activity is most likely
  • Discovery of genetic sequences linked to diseases

The limits of machine learning

Although machine learning is used widely and has tremendous potential, it is important to understand its limits. Machine learning, at this time, is not in any way a substitute for a human brain. It has very little flexibility to extrapolate outside of the strict parameters it learned and knows no common sense. With this in mind, one should be extremely careful to recognize exactly what the algorithm has learned before setting it loose in the real-world settings.

Without a lifetime of past experiences to build upon, computers are also limited in their ability to make simple common sense inferences about logical next steps. Take, for instance, the banner advertisements seen on many web sites. These may be served, based on the patterns learned by data mining the browsing history of millions of users. According to this data, someone who views the websites selling shoes should see advertisements for shoes, and those viewing websites for mattresses should see advertisements for mattresses. The problem is that this becomes a never-ending cycle in which additional shoe or mattress advertisements are served rather than advertisements for shoelaces and shoe polish, or bed sheets and blankets.

Many are familiar with the deficiencies of machine learning’s ability to understand or translate language or to recognize speech and handwriting. Perhaps the earliest example of this type of failure is in a 1994 episode of the television show, The Simpsons, which showed a parody of the Apple Newton tablet. For its time, the Newton was known for its state-of-the-art handwriting recognition. Unfortunately for Apple, it would occasionally fail to great effect. The television episode illustrated this through a sequence in which a bully’s note to Beat up Martin was misinterpreted by the Newton as Eat up Martha, as depicted in the following screenshots:


Screenshots from “Lisa on Ice” The Simpsons, 20th Century Fox (1994)

Machines’ ability to understand language has improved enough since 1994, such that Google, Apple, and Microsoft are all confident enough to offer virtual concierge services operated via voice recognition. Still, even these services routinely struggle to answer relatively simple questions. Even more, online translation services sometimes misinterpret sentences that a toddler would readily understand. The predictive text feature on many devices has also led to a number of humorous autocorrect fail sites that illustrate the computer’s ability to understand basic language but completely misunderstand context.

Some of these mistakes are to be expected, for sure. Language is complicated with multiple layers of text and subtext and even human beings, sometimes, understand the context incorrectly. This said, these types of failures in machines illustrate the important fact that machine learning is only as good as the data it learns from. If the context is not directly implicit in the input data, then just like a human, the computer will have to make its best guess.

Machine learning ethics

At its core, machine learning is simply a tool that assists us in making sense of the world’s complex data. Like any tool, it can be used for good or evil. Machine learning may lead to problems when it is applied so broadly or callously that humans are treated as lab rats, automata, or mindless consumers. A process that may seem harmless may lead to unintended consequences when automated by an emotionless computer. For this reason, those using machine learning or data mining would be remiss not to consider the ethical implications of the art.

Due to the relative youth of machine learning as a discipline and the speed at which it is progressing, the associated legal issues and social norms are often quite uncertain and constantly in flux. Caution should be exercised while obtaining or analyzing data in order to avoid breaking laws, violating terms of service or data use agreements, and abusing the trust or violating the privacy of customers or the public.

The informal corporate motto of Google, an organization that collects perhaps more data on individuals than any other, is “don’t be evil.” While this seems clear enough, it may not be sufficient. A better approach may be to follow the Hippocratic Oath, a medical principle that states “above all, do no harm.”

Retailers routinely use machine learning for advertising, targeted promotions, inventory management, or the layout of the items in the store. Many have even equipped checkout lanes with devices that print coupons for promotions based on the customer’s buying history. In exchange for a bit of personal data, the customer receives discounts on the specific products he or she wants to buy. At first, this appears relatively harmless. But consider what happens when this practice is taken a little bit further.

One possibly apocryphal tale concerns a large retailer in the U.S. that employed machine learning to identify expectant mothers for coupon mailings. The retailer hoped that if these mothers-to-be received substantial discounts, they would become loyal customers, who would later purchase profitable items like diapers, baby formula, and toys.

Equipped with machine learning methods, the retailer identified items in the customer purchase history that could be used to predict with a high degree of certainty, not only whether a woman was pregnant, but also the approximate timing for when the baby was due.

After the retailer used this data for a promotional mailing, an angry man contacted the chain and demanded to know why his teenage daughter received coupons for maternity items. He was furious that the retailer seemed to be encouraging teenage pregnancy! As the story goes, when the retail chain’s manager called to offer an apology, it was the father that ultimately apologized because, after confronting his daughter, he discovered that she was indeed pregnant!

Whether completely true or not, the lesson learned from the preceding tale is that common sense should be applied before blindly applying the results of a machine learning analysis. This is particularly true in cases where sensitive information such as health data is concerned. With a bit more care, the retailer could have foreseen this scenario, and used greater discretion while choosing how to reveal the pattern its machine learning analysis had discovered.

Certain jurisdictions may prevent you from using racial, ethnic, religious, or other protected class data for business reasons.  Keep in mind that excluding this data from your analysis may not be enough, because machine learning algorithms might inadvertently learn this information independently. For instance, if a certain segment of people generally live in a certain region, buy a certain product, or otherwise behave in a way that uniquely identifies them as a group, some machine learning algorithms can infer the protected information from these other factors. In such cases, you may need to fully “de-identify” these people by excluding any potentially identifying data in addition to the protected information.

Apart from the legal consequences, using data inappropriately may hurt the bottom line. Customers may feel uncomfortable or become spooked if the aspects of their lives they consider private are made public. In recent years, several high-profile web applications have experienced a mass exodus of users who felt exploited when the applications’ terms of service agreements changed, and their data was used for purposes beyond what the users had originally agreed upon. The fact that privacy expectations differ by context, age cohort, and locale adds complexity in deciding the appropriate use of personal data. It would be wise to consider the cultural implications of your work before you begin your project.

The fact that you can use data for a particular end does not always mean that you should.

How machines learn

A formal definition of machine learning proposed by computer scientist Tom M. Mitchellstates that a machine learns whenever it is able to utilize its an experience such that its performance improves on similar experiences in the future. Although this definition is intuitive, it completely ignores the process of exactly how experience can be translated into future action—and of course learning is always easier said than done!

While human brains are naturally capable of learning from birth, the conditions necessary for computers to learn must be made explicit. For this reason, although it is not strictly necessary to understand the theoretical basis of learning, this foundation helps understand, distinguish, and implement machine learning algorithms.

As you compare machine learning to human learning, you may discover yourself examining your own mind in a different light.

Regardless of whether the learner is a human or machine, the basic learning process is similar. It can be divided into four interrelated components:

  • Data storage utilizes observation, memory, and recall to provide a factual basis for further reasoning.
  • Abstraction involves the translation of stored data into broader representations and concepts.
  • Generalization uses abstracted data to create knowledge and inferences that drive action in new contexts.
  • Evaluation provides a feedback mechanism to measure the utility of learned knowledge and inform potential improvements. 

Keep in mind that although the learning process has been conceptualized as four distinct components, they are merely organized this way for illustrative purposes. In reality, the entire learning process is inextricably linked. In human beings, the process occurs subconsciously. We recollect, deduce, induct, and intuit with the confines of our mind’s eye, and because this process is hidden, any differences from person to person are attributed to a vague notion of subjectivity. In contrast, with computers these processes are explicit, and because the entire process is transparent, the learned knowledge can be examined, transferred, and utilized for future action.

Data storage

All learning must begin with data. Humans and computers alike utilize data storage as a foundation for more advanced reasoning. In a human being, this consists of a brain that uses electrochemical signals in a network of biological cells to store and process observations for short- and long-term future recall. Computers have similar capabilities of short- and long-term recall using hard disk drives, flash memory, and random access memory (RAM) in combination with a central processing unit (CPU).

It may seem obvious to say so, but the ability to store and retrieve data alone is not sufficient for learning. Without a higher level of understanding, knowledge is limited exclusively to recall, meaning exclusively what is seen before and nothing else. The data is merely ones and zeros on a disk. They are stored memories with no broader meaning.

To better understand the nuances of this idea, it may help to think about the last time you studied for a difficult test, perhaps for a university final exam or a career certification. Did you wish for an eidetic (photographic) memory? If so, you may be disappointed to learn that perfect recall is unlikely to be of much assistance. Even if you could memorize material perfectly, your rote learning is of no use, unless you know in advance the exact questions and answers that will appear in the exam. Otherwise, you would be stuck in an attempt to memorize answers to every question that could conceivably be asked. Obviously, this is an unsustainable strategy.

Instead, a better approach is to spend time selectively, memorizing a small set of representative ideas while developing strategies on how the ideas relate and how to use the stored information. In this way, large ideas can be understood without needing to memorize them by rote.


This work of assigning meaning to stored data occurs during the abstraction process, in which raw data comes to have a more abstract meaning. This type of connection, say between an object and its representation, is exemplified by the famous René Magritte painting The Treachery of Images:


Source: http://collections.lacma.org/node/239578

The painting depicts a tobacco pipe with the caption Ceci n’est pas une pipe (“this is not a pipe”). The point Magritte was illustrating is that a representation of a pipe is not truly a pipe. Yet, in spite of the fact that the pipe is not real, anybody viewing the painting easily recognizes it as a pipe. This suggests that the observer’s mind is able to connect the picture of a pipe to the idea of a pipe, to a memory of a physical pipe that could be held in the hand. Abstracted connections like these are the basis of knowledge representation, the formation of logical structures that assist in turning raw sensory information into a meaningful insight.

During a machine’s process of knowledge representation, the computer summarizes stored raw data using a model, an explicit description of the patterns within the data. Just like Magritte’s pipe, the model representation takes on a life beyond the raw data. It represents an idea greater than the sum of its parts.

There are many different types of models. You may be already familiar with some. Examples include:

  • Mathematical equations
  • Relational diagrams such as trees and graphs
  • Logical if/else rules
  • Groupings of data known as clusters

The choice of model is typically not left up to the machine. Instead, the learning task and data on hand inform model selection. Later in this article, we will discuss methods to choose the type of model in more detail.

The process of fitting a model to a dataset is known as training. When the model has been trained, the data is transformed into an abstract form that summarizes the original information.

You might wonder why this step is called training rather than learning. First, note that the process of learning does not end with data abstraction; the learner must still generalize and evaluate its training. Second, the word training better connotes the fact that the human teacher trains the machine student to understand the data in a specific way.

It is important to note that a learned model does not itself provide new data, yet it does result in new knowledge. How can this be? The answer is that imposing an assumed structure on the underlying data gives insight into the unseen by supposing a concept about how data elements are related. Take for instance the discovery of gravity. By fitting equations to observational data, Sir Isaac Newton inferred the concept of gravity. But the force we now know as gravity was always present. It simply wasn’t recognized until Newton recognized it as an abstract concept that relates some data to others—specifically, by becoming the g term in a model that explains observations of falling objects.


Most models may not result in the development of theories that shake up scientific thought for centuries. Still, your model might result in the discovery of previously unseen relationships among data. A model trained on genomic data might find several genes that, when combined, are responsible for the onset of diabetes; banks might discover a seemingly innocuous type of transaction that systematically appears prior to fraudulent activity; and psychologists might identify a combination of personality characteristics indicating a new disorder. These underlying patterns were always present, but by simply presenting information in a different format, a new idea is conceptualized.


The learning process is not complete until the learner is able to use its abstracted knowledge for future action. However, among the countless underlying patterns that might be identified during the abstraction process and the myriad ways to model these patterns, some will be more useful than others. Unless the production of abstractions is limited, the learner will be unable to proceed. It would be stuck where it started—with a large pool of information, but no actionable insight.

The term generalization describes the process of turning abstracted knowledge into a form that can be utilized for future action, on tasks that are similar, but not identical, to those it has seen before. Generalization is a somewhat vague process that is a bit difficult to describe. Traditionally, it has been imagined as a search through the entire set of models (that is, theories or inferences) that could be abstracted during training. In other words, if you can imagine a hypothetical set containing every possible theory that could be established from the data, generalization involves the reduction of this set into a manageable number of important findings.

In generalization, the learner is tasked with limiting the patterns it discovers to only those that will be most relevant to its future tasks. Generally, it is not feasible to reduce the number of patterns by examining them one-by-one and ranking them by future utility. Instead, machine learning algorithms generally employ shortcuts that reduce the search space more quickly. Toward this end, the algorithm will employ heuristics, which are educated guesses about where to find the most useful inferences.

Because heuristics utilize approximations and other rules of thumb, they do not guarantee to find the single best model. However, without taking these shortcuts, finding useful information in a large dataset would be infeasible.

Heuristics are routinely used by human beings to quickly generalize experience to new scenarios. If you have ever utilized your gut instinct to make a snap decision prior to fully evaluating your circumstances, you were intuitively using mental heuristics.

The incredible human ability to make quick decisions often relies not on computer-like logic, but rather on heuristics guided by emotions. Sometimes, this can result in illogical conclusions. For example, more people express fear of airline travel versus automobile travel, despite automobiles being statistically more dangerous. This can be explained by the availability heuristic, which is the tendency of people to estimate the likelihood of an event by how easily its examples can be recalled. Accidents involving air travel are highly publicized. Being traumatic events, they are likely to be recalled very easily, whereas car accidents barely warrant a mention in the newspaper.

The folly of misapplied heuristics is not limited to human beings. The heuristics employed by machine learning algorithms also sometimes result in erroneous conclusions. The algorithm is said to have a bias if the conclusions are systematically erroneous, or wrong in a predictable manner.

For example, suppose that a machine learning algorithm learned to identify faces by finding two dark circles representing eyes, positioned above a straight line indicating a mouth. The algorithm might then have trouble with, or be biased against, faces that do not conform to its model. Faces with glasses, turned at an angle, looking sideways, or with various skin tones might not be detected by the algorithm. Similarly, it could be biased toward faces with certain skin tones, face shapes, or other characteristics that do not conform to its understanding of the world.


In modern usage, the word bias has come to carry quite negative connotations. Various forms of media frequently claim to be free from bias, and claim to report the facts objectively, untainted by emotion. Still, consider for a moment the possibility that a little bias might be useful. Without a bit of arbitrariness, might it be a bit difficult to decide among several competing choices, each with distinct strengths and weaknesses? Indeed, some recent studies in the field of psychology have suggested that individuals born with damage to portions of the brain responsible for emotion are ineffectual in decision making, and might spend hours debating simple decisions such as what color shirt to wear or where to eat lunch. Paradoxically, bias is what blinds us from some information while also allowing us to utilize other information for action. It is how machine learning algorithms choose among the countless ways to understand a set of data.


Bias is a necessary evil associated with the abstraction and generalization processes inherent in any learning task. In order to drive action in the face of limitless possibility, each learner must be biased in a particular way. Consequently, each learner has its weaknesses and there is no single learning algorithm to rule them all. Therefore, the final step in the generalization process is to evaluate or measure the learner’s success in spite of its biases and use this information to inform additional training if needed.

Once you’ve had success with one machine learning technique, you might be tempted to apply it to everything. It is important to resist this temptation because no machine learning approach is the best for every circumstance. This fact is described by the No Free Lunch theorem, introduced by David Wolpert in 1996. For more information, visit: http://www.no-free-lunch.org.

Generally, evaluation occurs after a model has been trained on an initial training dataset. Then, the model is evaluated on a new test dataset in order to judge how well its characterization of the training data generalizes to new, unseen data. It’s worth noting that it is exceedingly rare for a model to perfectly generalize to every unforeseen case.

In parts, models fail to perfectly generalize due to the problem of noise, a term that describes unexplained or unexplainable variations in data. Noisy data is caused by seemingly random events, such as:

  • Measurement error due to imprecise sensors that sometimes add or subtract a bit from the readings
  • Issues with human subjects, such as survey respondents reporting random answers to survey questions, in order to finish more quickly
  • Data quality problems, including missing, null, truncated, incorrectly coded, or corrupted values
  • Phenomena that are so complex or so little understood that they impact the data in ways that appear to be unsystematic

Trying to model noise is the basis of a problem called overfitting. Because most noisy data is unexplainable by definition, attempting to explain the noise will result in erroneous conclusions that do not generalize well to new cases. Efforts to explain the noise will also typically result in more complex models that will miss the true pattern that the learner tries to identify. A model that seems to perform well during training, but does poorly during evaluation, is said to be overfitted to the training dataset, as it does not generalize well to the test dataset.


Solutions to the problem of overfitting are specific to particular machine learning approaches. For now, the important point is to be aware of the issue. How well the models are able to handle noisy data is an important source of distinction among them.

Machine learning in practice

So far, we’ve focused on how machine learning works in theory. To apply the learning process to real-world tasks, we’ll use a five-step process. Regardless of the task at hand, any machine learning algorithm can be deployed by following these steps:

  1. Data collection: The data collection step involves gathering the learning material an algorithm will use to generate actionable knowledge. In most cases, the data will need to be combined into a single source like a text file, spreadsheet, or database.
  2. Data exploration and preparation: The quality of any machine learning project is based largely on the quality of its input data. Thus, it is important to learn more about the data and its nuances during a practice called data exploration. Additional work is required to prepare the data for the learning process. This involves fixing or cleaning so-called “messy” data, eliminating unnecessary data, and recoding the data to conform to the learner’s expected inputs.
  3. Model training: By the time the data has been prepared for analysis, you are likely to have a sense of what you are capable of learning from the data. The specific machine learning task chosen will inform the selection of an appropriate algorithm, and the algorithm will represent the data in the form of a model.
  4. Model evaluation: Because each machine learning model results in a biased solution to the learning problem, it is important to evaluate how well the algorithm learns from its experience. Depending on the type of model used, you might be able to evaluate the accuracy of the model using a test dataset or you may need to develop measures of performance specific to the intended application.
  5. Model improvement: If better performance is needed, it becomes necessary to utilize more advanced strategies to augment the performance of the model. Sometimes, it may be necessary to switch to a different type of model altogether. You may need to supplement your data with additional data or perform additional preparatory work as in step two of this process.

After these steps are completed, if the model appears to be performing well, it can be deployed for its intended task. As the case may be, you might utilize your model to provide score data for predictions (possibly in real time), for projections of financial data, to generate useful insight for marketing or research, or to automate tasks such as mail delivery or flying aircraft. The successes and failures of the deployed model might even provide additional data to train your next generation learner.

Types of input data

The practice of machine learning involves matching the characteristics of input data to the biases of the available approaches. Thus, before applying machine learning to real-world problems, it is important to understand the terminology that distinguishes among 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; each person-year comprises of a person’s data for one year.

The unit of observation is related, but not identical, to the unit of analysis, which is the smallest unit from which the inference is made. Although it is often the case, the observed and analyzed units are not always the same. For example, data observed from people might be used to analyze trends across countries.

Datasets that store the units of observation and their properties can be imagined as collections of data consisting of:

  • Examples: Instances of the unit of observation for which properties have been recorded
  • Features: Recorded properties or attributes of examples that may be useful for learning

It is easiest to understand features and examples through real-world cases. To build a learning algorithm to identify spam e-mail, the unit of observation could be e-mail messages, the examples would be specific 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 the genomic markers from biopsied cells as well as the characteristics of patient such as weight, height, or blood pressure.

While examples and features do not have to be collected in any specific form, they are commonly gathered in matrix format, which means that each example has exactly the same features.

The following spreadsheet shows a dataset 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, while the columns record various each automobile’s features, such as price, mileage, color, and transmission type. Matrix format data is by far the most common form used in machine learning:

Features also come in various forms. If a feature represents a characteristic measured in numbers, it is unsurprisingly called numeric. Alternatively, if a feature is an attribute that consists of a set of categories, the feature is called categorical or nominal. A special case of categorical variables is called ordinal, which designates a nominal variable with categories falling in an ordered list. Some examples of ordinal variables include clothing sizes such as small, medium, and large; or a measurement of customer satisfaction on a scale from “not at all happy” to “very happy.” It is important to consider what the features represent, as the type and number of features in your dataset will assist in determining an appropriate machine learning algorithm for your task.

Types of machine learning algorithms

Machine learning algorithms are divided into categories according to their purpose. Understanding the categories of learning algorithms is an essential first step towards 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 hours.

Because predictive models are given clear instruction 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:

  • An e-mail message is spam
  • A person has cancer
  • A football team will win or lose
  • An applicant will default on a loan

In classification, the target feature to be predicted is a categorical feature known as the class, and is divided into categories called levels. A class can have two or more levels, and the levels may or may not be ordinal. Because classification is so widely used in machine learning, there are many types of classification algorithms, with strengths and weaknesses suited for different types of input data.

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 models are not the only type of numeric models, they are, by far, the most widely used. Regression methods are widely used for forecasting, as they quantify in exact terms the association between inputs and the target, including both, the magnitude and uncertainty of the relationship.

Since it is easy to convert numbers into categories (for example, ages 13 to 19 are teenagers) and categories into numbers (for example, assign 1 to all males, 0 to all females), the boundary between classification models and numeric prediction models is not necessarily firm.

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 often used for market basket analysis on retailers’ transactional purchase data. Here, the goal is 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 sunglasses, the retailer might reposition the items more closely in the store or run a promotion to “up-sell” customers on associated items.

Originally used only in retail contexts, pattern discovery is now starting to be used in quite innovative ways. For instance, it can be used to detect patterns of fraudulent behavior, screen for genetic defects, or identify hot spots for criminal activity.

The descriptive modeling task of dividing a dataset into homogeneous groups is called clustering. This is sometimes used for segmentation analysis that identifies groups of individuals with similar behavior or demographic information, so that advertising campaigns could be tailored for particular audiences. Although the machine is capable of identifying the clusters, human intervention is required to interpret them. For example, given five different clusters of shoppers at a grocery store, the marketing team will need to understand the differences among the groups in order to create a promotion that best suits each group.

Lastly, a class of machine learning algorithms known as meta-learners is not tied to a specific learning task, but is rather focused on learning how to learn more effectively. A meta-learning algorithm uses the result of some learnings to inform additional learning. This can be beneficial for very challenging problems or when a predictive algorithm’s performance needs to be as accurate as possible.

Machine learning with R

Many of the algorithms needed for machine learning with R are not included as part of the base installation. Instead, the algorithms needed for machine learning are available via a large community of experts who have shared their work freely. These must be installed on top of base R manually. Thanks to R’s status as free open source software, there is no additional charge for this functionality.

A collection of R functions that can be shared among users is called a package. Free packages exist for each of the machine learning algorithms covered in this book. In fact, this book only covers a small portion of all of R’s machine learning packages.

If you are interested in the breadth of R packages, you can view a list at Comprehensive R Archive Network (CRAN), a collection of web and FTP sites located around the world to provide the most up-to-date versions of R software and packages. If you obtained the R software via download, it was most likely from CRAN at http://cran.r-project.org/index.html.

If you do not already have R, the CRAN website also provides installation instructions and information on where to find help if you have trouble.

The Packages link on the left side of the page will take you to a page where you can browse packages in an alphabetical order or sorted by the publication date. At the time of writing this, a total 6,779 packages were available—a jump of over 60 percent in the time since the first edition was written, and this trend shows no sign of slowing!

The Task Views link on the left side of the CRAN page provides a curated list of packages as per the subject area. The task view for machine learning, which lists the packages covered in this book (and many more), is available at http://cran.r-project.org/web/views/MachineLearning.html.

Installing R packages

Despite the vast set of available R add-ons, the package format makes installation and use a virtually effortless process. To demonstrate the use of packages, we will install and load the RWeka package, which was developed by Kurt Hornik, Christian Buchta, and Achim Zeileis (see Open-Source Machine Learning: R Meets Weka in Computational Statistics 24: 225-232 for more information). The RWeka package provides a collection of functions that give R access to the machine learning algorithms in the Java-based Weka software package by Ian H. Witten and Eibe Frank. More information on Weka is available at http://www.cs.waikato.ac.nz/~ml/weka/

To use the RWeka package, you will need to have Java installed (many computers come with Java preinstalled). Java is a set of programming tools available for free, which allow for the use of cross-platform applications such as Weka. For more information, and to download Java on your system, you can visit http://java.com.

The most direct way to install a package is via the install.packages() function. To install the RWeka package, at the R command prompt, simply type:

> install.packages("RWeka")

R will then connect to CRAN and download the package in the correct format for your OS. Some packages such as RWeka require additional packages to be installed before they can be used (these are called dependencies). By default, the installer will automatically download and install any dependencies.

The first time you install a package, R may ask you to choose a CRAN mirror. If this happens, choose the mirror residing at a location close to you. This will generally provide the fastest download speed.

The default installation options are appropriate for most systems. However, in some cases, you may want to install a package to another location. For example, if you do not have root or administrator privileges on your system, you may need to specify an alternative installation path. This can be accomplished using the lib option, as follows:

> install.packages("RWeka", lib="/path/to/library")

The installation function also provides additional options for installation from a local file, installation from source, or using experimental versions. You can read about these options in the help file, by using the following command:

> ?install.packages

More generally, the question mark operator can be used to obtain help on any R function. Simply type ? before the name of the function.

Loading and unloading R packages

In order to conserve memory, R does not load every installed package by default. Instead, packages are loaded by users as they are needed, using the library() function.

The name of this function leads some people to incorrectly use the terms library and package interchangeably. However, to be precise, a library refers to the location where packages are installed and never to a package itself.

To load the RWeka package we installed previously, you can type the following:

> library(RWeka)

To unload an R package, use the detach() function. For example, to unload the RWeka package shown previously use the following command:

> detach("package:RWeka", unload = TRUE)

This will free up any resources used by the package.


To learn more about Machine Learning, the following books published by Packt Publishing (https://www.packtpub.com/) are recommended:

Resources for Article:

Further resources on this subject:


Please enter your comment!
Please enter your name here