February 6, 2021

Supervised vs. Unsupervised Learning

Supervised vs. Unsupervised Learning

The way machines learn can be broadly classified into two categories, namely, supervised learning and unsupervised learning. This post explores the difference between the two types of machine learning techniques.

What is Supervised learning?

It’s the more commonly used among the two as the majority of practical machine learning uses supervised learning. This form of machine learning technique is the process where an algorithm learns from labelled data and aims to predict an output based on new input data. Supervised Learning is similar to how a teacher guides and supplements the education of students, where the machine is taught using large volumes of data which have tagged with correct answers.

In this case, the data scientist usually acts as the ‘teacher’ in guiding the algorithm on the conclusions it should come up with.

The machine is firstly provided with a set of examples so that supervised learning algorithm analyzes the training data and produces a correct outcome based on what it’s fed.

For example, a classification algorithm will learn to identify an animal after being trained on a dataset of images that are properly labelled with the species of the animal and some identifying characteristics. When it’s shown a new image, the classification algorithm would compare it to the training examples to predict the correct label.

Image annotation is one of the main methods which computer vision models learn.

A classification algorithm will learn to identify an animal after being trained on a dataset of images that are properly labelled with the species of the animal

What is Unsupervised Learning?

Meanwhile, unsupervised learning is the training of machines using unlabeled data. In contrast to supervised learning, there are no output categories or labels on the training data, so the machine receives a training dataset with no explicit instructions or guidance. This allows the algorithm free rein to analyze the information.

The main duty of the machine is to group the unsorted information according to a few criteria that it will uncover, such as similarities, patterns and differences.  Essentially, the training dataset in unsupervised learning is a collection of examples without a particular desired outcome or correct answer.

Image source: boozallen

The machine will group the unsorted information according to a few criteria that it will uncover, such as similarities, patterns and differences.

While this form of machine learning technique is mainly used in pattern detection and descriptive modelling. It is useful for customer segmentation as it can segment the information into clusters than can then be further analyzed to identify patterns that may not have been considered due to pre-existing biases.

There is no better way among the two machine learning technique. Although there may be times when both are integrated to reach an end result. However, every algorithm learns differently, the decision to use either supervised or unsupervised learning for your machine learning algorithm is typically dependent on the various factors that are related to the structure and volume of data and the use case of issue.

What is Semi-supervised learning?

In semi-supervised learning, a model is trained using a combination of labeled and unlabeled data. This approach is particularly useful when obtaining large amounts of labeled data is expensive or time-consuming, but unlabeled data is readily available.

Semi-supervised learning leverages the additional unlabeled data to improve the model's performance. The idea is that the model can learn more robust and generalizable representations by utilizing the information present in the unlabeled data. This can lead to better performance, especially in cases where the labeled dataset is small or may not fully represent the underlying data distribution.

One common approach to semi-supervised learning is to combine a supervised loss (using labeled data) with an unsupervised loss (using unlabeled data). This encourages the model to learn features that are both discriminative for the task at hand and representative of the data distribution.

Learn more about data labeling use cases.

Bryce Wilson
Data Engineer at Black.ai

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