Machine learning is a rapidly growing field that is transforming the way we live and work. At its core, machine learning is all about teaching computers to learn from data, allowing them to make predictions or take actions based on that data. It has the potential to automate many tasks that were previously performed by humans, freeing up time and resources for more creative and complex pursuits.
In recent years, machine learning has been used in a variety of industries, from healthcare and finance to retail and transportation, leading to the development of new products and services and the improvement of existing ones.
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.
This article provides an introduction to supervised learning, including its definition, key concepts, and problems. It will also discuss the ways in which supervised learning is being applied in various industries and the potential benefits and challenges associated with it.
What is machine learning in general?
Machine learning is a subset of artificial intelligence that enables computers to learn and improve from experience without being explicitly programmed. Unlike traditional programming, where a programmer writes code to perform a specific task, in machine learning, the system uses statistical algorithms to analyze data and improve its performance over time.
Machine learning algorithms can identify patterns and relationships in data, make predictions, and make decisions based on that data. This approach differs from traditional computer programming, which relies on predetermined rules, algorithms and heuristics, and does not adapt to new data or changing conditions. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. Machine learning programs can perform tasks without being explicitly programmed to do so.
In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step.
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 models
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
In few words we could say that supervised learning is used for prediction problems, unsupervised learning is used for understanding the structure of the data, and reinforcement learning is for decision-making in complicated situations.
Supervised learning, same as supervised machine learning, is based on cultivating data and generating an output from past experiences (labeled data).
That means the input data consists of labeled examples: each data point is a pair of data example (input object) and target label (desired to be predicted).
In supervised learning, an input variable is mapped to an output variable with the help of a mapping function that is learned by an ML model. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias). This statistical quality of an algorithm is measured through the so-called generalization error. The goal of testing data is to estimate generalization error on unlabeled datasets.
Of course, all that is possible when the machine learning model is provided with quality training data. The latter can lead to drastic improvements in model performance, giving you a considerable edge over your competitors.
As supervised learning model’s ability to accumulate training data and utilize performance criteria derives from previous experiences, the same data gets employed to forecast future events and refine present training data. This process ends up saving lots of time and effort, not to mention how helpful it gets in solving many real-world computation problems.
In a sense, the supervised learning process starts with the collection and preparation of labeled training data, and once that data is accumulated, the labeled data gets categorized into different groups/versions.
Supervised learning problems
Supervised learning is the common name for a large section of machine learning, and it can be broken down into the following subcategories.
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.
Supervised learning’s classification model gets to predict which category the data is part of. For example, judging whether an email is spam or not is an example of binary classification. It means that there are two classes for the model to pick from: spam and not spam.
Multi-class classification models, on the other hand, are used to classify data into more than two classes, such as species of animals.
Multi-label classification is another type of classification task. It is similar to multi-class models, but it allows a single data point to belong to multiple classes simultaneously. For example, a single image can be labeled as both "dog" and "cat."
In general we could conclude that supervised classification is the most popular approach and many real-world problems could be reduced to binary, multi-class or multi-label classification.
To learn how to create a text classification model check our our tutorial.
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.
Of course there may be different views on the same problem and the same task may be solved in many different ways. An example of a machine learning problem that can be stated as either classification or regression is predicting the price of a house based on its features.
For a classification approach, the price of the house can be divided into discrete categories, such as "cheap," "affordable," and "expensive." The goal of the algorithm would be to predict which category the house belongs to based on its features.
For a regression approach, the price of the house can be treated as a continuous value. The goal of the algorithm would be to predict the exact price of the house based on its features.
In both cases, the features of the house, such as its size, location, and number of rooms, would be used as inputs to the algorithm, and the target output would be the price or the price category of the house. The key difference between the two approaches is the type of target output — a categorical value for classification and a continuous value for regression.
Supervised learning algorithms
It’s quite simple, the aim of supervised learning algorithms is to comprehend which steps must be taken to help the user attain their ultimate result. As supervised learning mainly addresses two general sorts of issues, regression and classification, there are a number of different supervised learning model types. Let’s explore some of the most commonly used ones.
In most cases, linear regression is considered as one of the most popular and simple algorithms in both machine learning and statistics. Mainly used to predict future results, the linear regression supervised learning algorithm is referred to identify the link between a dependent variable and one or more other independent variables by offering a sloped straight line to explain the connection between any given variable. To simplify it, linear regression is a statistical procedure that gets employed for predictive analysis; it is used to predict sales, product pricing, age, and so on.
When there is a single independent variable and one dependent variable, it is referred to as simple linear regression, and when independent variables are added, the process becomes a multiple linear regression.
Similar to linear regression, logistic regression models try to recognize relationships among data inputs. Logistic regression is mainly used to resolve binary classification issues, including spam identification, and it is commonly used when the dependent variable has binary outputs, such as yes and no, or true and false. It is considered as one of the favorable classification algorithms for its ability to come up with probabilities and categorize new data by referring to continuous and discrete datasets.
It is also helpful to keep in mind that logistic regression is separated into three categories, binomial, ordinal and multinomial.
Support vector machine
The support vector machine is used for both data regression and classification, but it is mostly referred to solve classification issues. When faced with classification issues, this supervised learning algorithm builds a hyperplane, also known as the decision boundary; it separates the two classes of data points on each of two sides of the plane.
The support vector machine picks the extreme vectors, also known as support vectors (hence the name), and they assist in the creation of the hyperplane. There are two types of support vector machines, linear support vector machine, which is employed for linearly separable data, and non-linear support vector machine which is referred to when working with non-linearly separated data.
Neural networks are a type of machine learning algorithm that resemble the structure and function of the human brain. They consist of interconnected nodes, or artificial neurons, that process information and make predictions.
Neural networks can be used for a wide range of tasks, including image and speech recognition, natural language processing, and decision-making.
One of the key strengths of neural networks is their ability to learn and improve over time through a training process, where the network adjusts its weights and biases based on the input data. This allows neural networks to handle complex, non-linear relationships in the data and make accurate predictions.
However, designing and training neural networks can be a time-consuming and computationally hard process, and the choice of architecture, activation functions, and optimization algorithms can greatly affect their performance.
Deep learning is a subfield of machine learning that focuses on the development of multi-layered artificial neural networks.
These networks are called "deep" because they contain multiple hidden layers between the input and output layers, allowing them to learn hierarchical representations of the data. This makes deep learning algorithms well-suited for tasks such as image classification, speech recognition, and natural language processing, where the data has a complex structure and high-level features can be learned from lower-level ones.
Deep learning algorithms have been able to achieve state-of-the-art results in many areas and are now being used in a wide range of applications, from self-driving cars to medical diagnosis.
The main challenge in deep learning is the need for large amounts of labeled training data and computing resources, as well as the risk of overfitting if the model is too complex. However, advances in hardware and algorithms have made it possible to train deep neural networks on massive datasets, leading to continued growth and success in the field of deep learning.
A decision tree is one of the most popular supervised machine learning algorithms that is used for solving both regression and classification problems.
It is a tree-based model that divides the data into smaller subsets and makes a prediction by following a series of decisions based on the input features. Each node in the tree represents a test on one of the features, and the branches represent the outcome of that test.
The end of the branches is represented by a prediction or a class label.
Decision trees are easy to understand and interpret, and they can handle both categorical and numerical data.
The most popular approach is to use decision trees in ensemble algorithms such as random forest or gradient boosting.
They are also robust to outliers and missing values, making them a popular choice for a variety of applications in different industries.
K-nearest neighbors (KNN) is a simple yet powerful machine learning algorithm used for both classification and regression problems. The basic idea behind KNN is that a data point can be classified or its value can be predicted by looking at the K nearest data points in its feature space.
The algorithm works by storing all the available data and then, for a new data point, finding the K data points in the stored data that are closest to it in terms of distance. The prediction is then based on the majority class of the K nearest neighbors or the average of their values, depending on the problem type.
KNN is easy to implement and has relatively low computational costs, making it a popular choice for applications in fields such as image and speech recognition, medical diagnosis, and finance. However, its accuracy can be affected by the choice of K and the type of distance metric used.
Supervised learning and its alternatives
Who said that models can only learn based on labeled data? This is where unsupervised machine learning steps in.
If a supervised learning model 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.
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.
- Unsupervised learning is especially helpful for beginners, especially those who are in the field of data science, as they can witness how it analyzes raw input data.
With all mentioned above, it is safe to state that one of the main differences between supervised and unsupervised learning models is the way their algorithms are trained. How supervised learning models explore and acquire data is quite simple, as they have the freedom to do so. Whereas in the case of unsupervised learning algorithms, they deal with unlabelled data as a training set.
Since the output is unknown in unsupervised machine learning, the training becomes more complicated, not to mention that it also needs to work with numerous unclassified datasets and recognize the new patterns in them.
Here we could briefly describe two major parts of unsupervised learning: clustering and association.
Clustering entails finding a pattern in a collection of uncategorized data. Clustering algorithms process data and find natural clusters existing in the data. Computer vision engineers can also modify how many clusters the algorithm should identify. Any detail on these clusters can be adjusted accordingly.
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.
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.
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.
Let's take an example of a photo archive that contains both labeled and unlabeled images. Some part of data is already tagged.
The concept of semi-supervised learning is fairly simple; the user manually labels a small portion of data, instead of providing tags to the whole dataset.
Later, the same labeled data gets to be used as a data model, which then gets to be applied to large amounts of unlabeled data. Semi-supervised learning works with little labeled data and large unlabeled data, which minimizes the cost of manual annotation and cuts down on the time of data preparation.
Reinforcement learning uses observation gathered from the interaction with the environment to act in a way that maximizes the reward and minimizes the risk. As an algorithm (also called the agent), it continuously studies its environment until it explores all possibilities.
Reinforcement learning has the ability to solve various complex issues that no other machine learning algorithm can. It 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.
Some of the benefits of reinforcement learning include focusing on an issue as a whole as opposed to splitting it into several small-scale problems, acquiring data directly from the interactions it has with agents and its surroundings, and its ability to adapt and operate in different environments.
Reinforcement learning continues to be one of the hottest research topics out there and is yet on its way to finding widespread adoption.
Advantages of supervised machine learning algorithms
There are a handful of benefits to the supervised machine learning algorithms:
- Predictive accuracy: A supervised learning model can achieve high predictive accuracy when trained on a large and diverse labeled dataset. This makes it well suited for applications where the goal is to make accurate predictions. In many cases, the machine gets to execute the job faster and more accurately than humans.
- Clear objectives: classes and possible values of the training data are known and supervised learning has a clear objective of learning the mapping between inputs and outputs, which makes it easier to evaluate the performance of the algorithm and optimize it for a specific task.
- Wide range of applications: Supervised learning can be applied to a wide range of problems, including classification, regression, and structured prediction. This makes it a versatile and flexible method for a variety of tasks.
- Easier to implement: Supervised learning models are generally easier to implement and understand compared to unsupervised algorithms, making it a more accessible option for many practitioners. Besides, a large pool of algorithms is available.
While unsupervised learning has its own advantages, such as the ability to uncover hidden patterns in the data, supervised learning is still much more widespread for solving most real-world problems.
Disadvantages of supervised learning
The main problem with supervised learning is the need for labeled data. In order to train a supervised learning algorithm, you need a large and diverse labeled dataset that includes both inputs and the corresponding outputs. This can be difficult and time-consuming to obtain, especially for complex tasks.
Sometimes you could find data without manual annotations – for example, search engines, recommendation systems, stock prices or bank defaults. This data is already tagged.
But in many cases it is very difficult or even impossible to find such labeled data in real world. So the data should be annotated manually. All the disadvantages of supervised learning techniques come from this fact.
- The performance of supervised learning models is highly dependent on the quality of the training data provided.
- It's challenging and time-consuming to label massive data in supervised machine learning.
- It's extremely 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 machine learning cannot classify data on its own.
- Its inability to complete complex texts is considered one of the biggest supervised learning problems.
- As supervised learning acquires all its knowledge from human input, the likelihood of human error can be high.
- Being trained on manually annotated data models, can suffer from a lack of diversity in the training dataset, which can lead to biased models that do not reflect the true distribution of the data. This can result in poor performance on underrepresented or minority groups.
Annotating training data and building a supervised learning model with SuperAnnotate
As we mentioned above, using supervised learning approach is the most common way of building machine learning models.
There are a lot of advantages to this but in many cases it is difficult to find labeled data without manual annotation.
Actually manual data annotation sometimes is the most difficult part of an AI solution.
Using appropriate toolset for annotation could save a lot of time and resources. SuperAnnotate's platform allows to annotate different types of data such as images, text, and video, audio, LiDAR, and DICOM.
There are many different types of annotation supported by SuperAnnotate. Using the platform one could get labeled data and build a model for many supervised learning tasks:
- Text classification
- Image classification
- Information extraction
To learn more about the use cases SuperAnnotate covers, request a demo.
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 how supervised machine learning models became so widespread across different applications: while data-driven and human-dependent, they provides hands-on solutions across different industries. We hope this article expands your understanding of supervised learning and its usage. Don't hesitate to reach out, if we can be of further help.