Supervised learning is one of the most widely practiced branches of machine learning (ML) that uses labeled training data to help models make accurate predictions. The training data here serves as a supervisor and a teacher for the machines, hence the name. A similar methodology is instrumental in solving real-world challenges such as image classification, spam filtering, risk assessment, fraud detection, etc. We’ll get down to the nitty-gritty of how supervised learning works and its alternatives in the proceeding sections:

  • Types of machine learning algorithms
  • Supervised learning
  • Why supervised learning
  • How it works
  • Types of supervised learning techniques
  • Advantages of supervised learning
  • Disadvantages of supervised learning
  • Supervised learning and its alternatives
  • Key takeaways

Types of machine learning algorithms

Machine learning algorithms are grouped by their purpose and similarity. Opinions split when it comes to defining categories, but generally speaking, we can identify four types of machine learning tasks:

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning
machine learning categorization

Supervised learning

Supervised learning is based on cultivating data and generating an output from past experience (labeled data). In supervised learning, an input variable(x) is mapped to an output variable(y) with the help of a mapping function that is learned by an ML model.

Y= f(x)

Here the model creates the function that connects the two variables with the ultimate objective to predict the correct label for the input data, which may take plenty of time, iterations, and data revision from you.

Why supervised learning?

As the global market for machine learning is expected to expand by a 42% compound annual growth rate (CAGR) before 2024, supervised learning, as a fundamental ML methodology, becomes more relevant than ever. Its ability to turn data into actionable insights to achieve the desired outcomes for the target variable benefits an increasing number of industries. Of course, all that is possible when provided quality training data. The latter can lead to drastic improvements in model performance, giving you a considerable edge over your competitors.

How it works

A supervised learning algorithm always has a target or outcome variable (or dependent variable), which is detected from a provided set of predictors (independent variables). The algorithm uses this set of variables to create a function that maps inputs to desired outputs. This training process is repeated for as long as it takes for the model to achieve a high level of accuracy.

classification and regression

Types of supervised learning techniques

Supervised learning itself can be broken down into the following subcategories:

Classification model

During training, engineers give the algorithm data points with an assigned class or category. With classification, an input value is taken and assigned a class or category, depending on the training data provided. For example, judging whether an email is spam or not is an example of classification. The two classes to pick from (spam, or not spam) is called binary classification. If there are more than two classes to choose from, we call it multiclass classification. Common classification algorithms include support vector machines, random forest, decision trees, and k-nearest neighbors.

Regression model

The main difference between regression and classification models is that regression algorithms are used to predict continuous values (test scores), while classification algorithms predict discrete values (spam/not spam, male/female, true/false). Regression is a statistical process that finds a significant relationship between dependent and independent variables. As an algorithm, it predicts a continuous number. For example, you may use a regression algorithm to determine a student’s test grade depending on the number of hours they studied that week. In this situation, the hours studied become the independent variable, and the student’s final test score is the dependent variable. You can draw a line of best fit through different data points to show the model’s predictions when a new input is introduced. The same line can also be used to predict test scores based on another student’s performance. Common regression algorithms include linear regression, polynomial regression, and regression trees.

Advantages of supervised learning

  • The model learns from past experiences, i.e., the introduced data.
  • Availability of a significantly larger pool of algorithms than in the case of unsupervised learning.

Disadvantages of supervised learning

  • It’s challenging and time-consuming to label massive data in supervised machine learning.
  • It’s very hard to predict the correct output in supervised machine learning if the distribution of the test data differs significantly from that of the training dataset.

Supervised learning and its alternatives

Models can learn not just based on labeled data. This is where unsupervised learning steps in. If supervised learning uses labeled input and output data, an unsupervised learning algorithm works on its own to discover the structure of unlabeled data. Unsupervised learning comes in handy when the human expert has no idea what to look for in the data. Unlike supervised learning, it is best suited for more complex tasks, including descriptive modeling and pattern detection.

Unsupervised learning

Here are a few must-knows about unsupervised learning:

  • Unsupervised learning is particularly useful in finding unknown patterns in a dataset.
  • It aids in finding features needed for categorization.
  • Your images, videos, or any data provided doesn’t have to be annotated or labeled.

Types of unsupervised learning

Unsupervised learning, in its turn, is divided into the following:

  • Clustering

Clustering entails finding a pattern in a collection of uncategorized data. Clustering algorithms process data and find natural clusters existing in the data. CV engineers can also modify how many clusters the algorithm should identify. Any detail on these clusters can be adjusted accordingly.

  • Association

The association technique concerns finding relationships that exist between variables in large databases. Experts can easily establish associations among data objects. For instance, individuals who buy a new house are most likely to buy new furniture. K-means clustering and association rules are common unsupervised learning algorithm examples.

Semi-supervised learning

In the previous two machine learning types, there is either labeled or unlabeled data to assist training. Semi-supervised machine learning lies between the two techniques. Data labeling is an expensive and time-consuming process that requires highly-trained human resources. In that regard, there are cases where labels are unavailable in most observations but present in just a handful, and this is where semi-supervised machine learning comes in. Let's take an example of a photo archive that contains both labeled and unlabeled images. Semi-supervised machine learning attempts to solve problems that lie between supervised and unsupervised learning by discovering and learning the structure of the input variables.

Reinforcement learning

Reinforcement learning uses observation gathered from the interaction with the environment to act in a way that maximizes the reward or minimizes the risk. As an algorithm (also called the agent), it continuously studies its environment until it explores all possibilities. Reinforcement learning allows machines to automatically determine the ideal behavior in a given context to achieve maximum performance. Common algorithms in this category include q-learning, temporal difference, and deep adversarial networks. These algorithms cover areas such as autonomous vehicles, robotic hands, and computer-played board games. Reinforcement learning continues to be one of the hottest research topics out there and is yet on its way to finding widespread adoption.

engineering process

Key takeaways

A quick recap on ML algorithms:

  • Supervised learning: algorithms use labeled data to predict the output from the input data.
  • Unsupervised learning: a model is trained using unlabeled data, which is easy to collect and store.
  • Semi-supervised learning: falls between supervised learning and unsupervised learning. Machines are trained using both labeled and unlabeled data.
  • Reinforcement learning: uses observations gathered from the interaction to maximize the reward in a particular situation.  

Little wonder why supervised learning is this common in application: while data-driven and human-dependent, it provides hands-on solutions across different industries. We hope this article expands your understanding of supervised learning and its applications. Don't hesitate to reach out, if we can be of further help.

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