What is Supervised Learning?

Machine learning is a tremendously powerful paradigm, making businesses and people who use it more productive and efficient. According to a survey by S&P Global Intelligence, 59% of enterprises worldwide either have deployed a machine learning initiative or have one in the pipeline.

At its heart, machine learning seeks to make computers more intelligent, enabling them to learn to improve their performance without being explicitly programmed to do so. However, there are multiple ways to accomplish this task, including two approaches known as supervised and unsupervised learning. So what is supervised learning, and why is supervised learning such a crucial machine learning technique?

What Is the Definition of Supervised Learning?

Supervised learning is a subfield of machine learning in which researchers train a model to make decisions based on labeled data. During the learning process, the model receives a series of data points, which it sees one after the other. Each data point contains the input itself as well as the correct label to assign to that input. These data points are known as the training set.

A supervised learning model contains parameters that it applies to each input, calculating a final prediction about the label for that input. If this guess is incorrect, the model adjusts its parameters to make the correct estimate more likely; if the prediction matches the right label, no action is necessary.

Once the training process is complete, researchers evaluate the model’s performance on a separate dataset, known as the test set. This dataset does not contain data points from the training set, so the model can be tested on fresh inputs.

Supervised learning is contrasted with unsupervised learning, another machine learning paradigm. Unlike supervised learning, models are not shown training examples in unsupervised learning. Rather, researchers provide the complete dataset to the model without any labels, and the model needs to discover patterns and relationships in the data independently.

Supervised Learning: Examples

Suppose you wanted to train a model to distinguish between images of cats and images of dogs. To do so using supervised learning, you would first need to accumulate a dataset of images, some containing cats and others containing dogs.

Each image would then need to be labeled based on its contents. Under the hood, these labels are represented as numerical inputs (e.g., 0 indicates the image has a cat, and 1 indicates the presence of a dog). To make the model more robust, you might also wish to include images with neither cats nor dogs or images where both are present and give them their own labels.

Machine learning researchers have provided several datasets for supervised learning in real-world image-recognition use cases. For example, the ImageNet database contains more than 14 million labeled images, while the CIFAR-10 dataset is restricted to only ten labels (e.g., pictures of airplanes, cars, birds, and horses).

Another prevalent example of supervised learning used daily is email spam filters. Email providers like Gmail and Yahoo must protect users’ inboxes by redirecting suspected spam to a separate folder. To detect spam, machine learning researchers can show models examples of both spam and legitimate emails, teaching it to differentiate between them over time. These spam classification models examine incoming messages’ text, links and images to predict whether the messages are wanted or unwanted.

Why Is Supervised Learning Important?

Machine learning is a crucial data science technology, and supervised learning is the most widely used machine learning approach. Supervised learning can predict both discrete values (e.g., detecting whether a financial transaction is fraudulent) and continuous values (e.g., estimating home prices based on a house’s location and its number of bedrooms).

Deep learning, a machine learning technique that trains huge models known as neural networks, is one example of supervised learning. Models trained using supervised learning have been successful in a wide range of applications, including image classification, speech recognition and natural language processing.

Because of the popularity of supervised learning, researchers and developers have created many different machine learning libraries, frameworks and tools to apply supervised learning algorithms. One strong alternative is scikit-learn, a machine learning library for the Python programming language. With scikit-learn, users can deploy many supervised learning techniques, including support vector machines, gradient descent and decision trees.

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