Supervised Machine Learning

What Is Supervised Machine Learning?

Supervised machine learning is a system of machine learning that uses labeled datasets, i.e. collective points of data whose information has been annotated by humans, to help the machine learning (ML) software’s algorithms infer categorizations, classifications, and/or predictions.

Given that machine learning can either be supervised, semi-supervised, or unsupervised in terms of their data labeling requirements, supervised machine learning is, naturally, the opposite form to unsupervised machine learning. What does it mean for ML to be supervised or unsupervised, however?

How Does Supervised Machine Learning Work?

Supervised machine learning (SML) works by the given algorithms and software forming an output — the algorithm’s generated inference — based on a specific input, i.e. the training set.

The training set is the collection of labeled datasets that are largely – if not completely – provided by humans. Note that these labeled datasets are considered the baseline of truth in the context of ML. In other words, regardless of whether or not the human data labelers are correct in their data annotation, the ML system has no ability to gauge this accuracy and makes its inferences based on their inputted data.

In addition to training sets, SML systems are tested for accuracy based on datasets known as testing sets: these are used by humans to test the accuracy of a supervised machine learning system. The human testers have an expected outcome, and the evaluation process involves cross-checking the results of test sets with the expected results for the training sets.

What Are the Different Types of Supervised Learning in Machine Learning?

Supervised learning has many different types in the context of machine learning because there are so many ways that humans can interact with ML systems based on how the given data labeling process takes place.

There are two core types of supervised machine learning based on the problems that they tackle: classification and regression. However, the numerous methods to tackle these problems mean that there are many subcategories of supervised learning approaches.

What Is Classification?

Classification refers to the problem of ensuring that SML algorithms correctly assign a class label to their datasets. As such, classification algorithms need to be trained by data labelers to ensure that supervised learning software categorizes its input based on certain criteria.

For instance, image classification can involve an SML system determining how best to transcribe audio content. Words like tour and chore may sound similar, especially to a machine, so it is down to data labelers to ensure the training datasets can distinguish between the two.

There are many examples of supervised learning algorithms that tackle the problem of classification, such as:

• decision trees
• random forest
• neural networks
• gradient boosting
• support vector machines
• naive Bayes

Determining which kind of algorithm is best depends entirely on the nature of the input data that needs to be classified. Different data structures and input methods will require some shopping around to find the ML that is the most convenient to understand and access.

What Is Regression?

Regression refers to the problem of ensuring that SML algorithms correctly determine the relationship that occurs between independent and dependent variables. Examples of independent and dependent variables respectively are: a type of medicine and a patient’s health; an exercise plan and a person’s fitness level; and a budgetary plan and a consumer’s purchasing habits.

Data labelers must train an SML system to algorithmically determine the dynamic that such dependent variables and independent variables have to each other. By learning the relationship between two data points, the system can then take a new data point and form calculations – in the form of forecasts and predictions – according to the historically expected outcome.

For example, consider data labelers inputting the location of houses and their changing prices over time. If properly trained, an SML system could begin to forecast housing prices based on the patterns that emerge in these variables over time.

The types of supervised learning algorithms that tackle the problem of regression are large and ever-growing, but prominent examples include:

• linear regression
• logistic regression
• polynomial regression
• elastic net regression
• ridge regression
• lasso regression

As with ML algorithms designed for classification, depending on the different type, amount, and organization of data points being scrutinized, one of these models may prove more effective and useful than others.

Why Is Supervised Machine Learning Important?

Supervised machine learning is important because it allows humans and software systems to form a symbiotic relationship: humans feed the software valuable labeled data and the software makes inferences accordingly.

Such a dynamic allows data labelers, software designers, and many more professionals to better understand how to both train and learn from machine learning technologies.

As such, SML is a crucial means to not only increase our access to various forms of knowledge and insights, but also increase our understanding of how machine learning can be enhanced with careful human intervention.

In practical terms, supervised machine learning also facilitates automation in a great number of business functions, cutting down on manual resources required. This extends to integral functions like marketing, sales, and of course, security and fraud prevention. SEON, of course, leverages regression-based machine learning extensively to identify potentially fraudulent behavior, but the technique also is a crucial part of things like predicting customer lifetime values, automated targeted marketing, sentiment analysis, and much more.

How Can Supervised Machine Learning Fight Fraud?

Supervised machine learning can fight fraud by gathering and processing transactional data and forming inferences based on the information that can be used to determine what constitutes both legitimate and illegitimate financial activity.

For example, SML systems can be fed long-term data, i.e. historical data sets, that represent an ecommerce website’s transactional history. When supervised machine learning software has data labelers who input the year-by-year transactions of that ecommerce site, it becomes equipped to spot anomalies – and therefore suspicious account activity – and even make predictions about when fraudulent transactions may occur again.

Other ways that SML can fight fraud include:

• Reducing false positives: By being supervised, humans can train the system to avoid flagging non-suspicious accounts.
• Enhancing data enrichment: Humans can augment the data and train the system to do the same.
• Improving fraud scoring: The intuition of humans combined with the efficiency of an automated AML system improves SML’s ability to determine the fraud score of an account.

What Are the Use Cases for Supervised Machine Learning?

The use cases of supervised machine learning are ever-growing among all kinds of industries and areas, but we will focus on its use cases that fall into the context of fraud prevention, resource management, and ecommerce here.

Fraud Prevention

Aside from its fraud fighting benefits of reducing false positives, enriching data, and improving fraud scoring, SML also offers many fraud prevention use cases, such as:

• carrying out predictive modeling to help find signs of future fraud attacks
• utilizing anomaly detection to help detect potential fraudsters
• identifying the fraud risks of various accounts through the process of behavioral analysis

Resource Management

SML is able to improve resource management – i.e. the allocation of resources, such as staff and equipment, to any given project – by carrying out the following:

• automating the process of logistics planning when assigning work schedules to staff
• gathering and acting on data that represents the best allocation of resources
• expediting – and potentially reducing human biases in – the process of people analytics, i.e. the collating of staff data, such as their job performance, call time, and so on

Ecommerce

Supervised machine learning is able to improve the process of online shopping thanks to the fact that it can achieve the following:

• reductions in both customer friction and customer churn
• enhancements to product recommendations
• personalized online advice for consumer

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