Difference Between Supervised and Unsupervised Learning

Introduction

Machine learning is already an important part of how modern organisation and services function. Whether in social media platforms, healthcare, or finance, machine learning models are deployed in a variety of settings. But the steps needed to train and deploy a model will differ depending on the task at hand and the data that’s available.

Supervised and unsupervised learning are examples of two different types of machine learning model approach. They differ in the way the models are trained and the condition of the training data that’s required. Each approach has different strengths, so the task or problem faced by a supervised vs unsupervised learning model will usually be different.

What is Supervised Learning?

Supervised machine learning requires labelled input and output data during the training phase of the machine learning lifecycle. This training data is often labelled by a data scientist in the preparation phase, before being used to train and test the model. Once the model has learned the relationship between the input and output data, it can be used to classify new and unseen datasets and predict outcomes.

The reason it is called supervised machine learning is because at least part of this approach requires human oversight. The vast majority of available data is unlabelled, raw data. Human interaction is generally required to accurately label data ready for supervised learning. Naturally, this can be a resource intensive process, as large arrays of accurately labelled training data is needed.

Supervised machine learning is used to classify unseen data into established categories and forecast trends and future change as a predictive model. A model developed through supervised machine learning will learn to recognise objects and the features that classify them. Predictive models are also often trained with supervised machine learning techniques. By learning patterns between input and output data, supervised machine learning models can predict outcomes from new and unseen data. This could be in forecasting changes in house prices or customer purchase trends.

Supervised machine learning is often used for:

• Classifying different file types such as images, documents, or written words.
• Forecasting future trends and outcomes through learning patterns in training data.

What is Unsupervised Learning?

Unsupervised machine learning is the training of models on raw and unlabelled training data. It is often used to identify patterns and trends in raw datasets, or to cluster similar data into a specific number of groups. It’s also often an approach used in the early exploratory phase to better understand the datasets.

As the name suggests, unsupervised machine learning is more of a hands-off approach compared to supervised machine learning. A human will set model hyperparameters such as the number of cluster points, but the model will process huge arrays of data effectively and without human oversight. Unsupervised machine learning is therefore suited to answer questions about unseen trends and relationships within data itself. But because of less human oversight, extra consideration should be made for the explainability of unsupervised machine learning.

The vast majority of available data is unlabelled, raw data. By grouping data along similar features or analysing datasets for underlying patterns, unsupervised learning is a powerful tool used to gain insight from this data. In contrast, supervised machine learning can be resource intensive because of the need for labelled data.

Unsupervised machine learning is mainly used to:

• Cluster datasets on similarities between features or segment data
• Understand relationship between different data point such as automated music recommendations
• Perform initial data analysis

Comparison Table Between Supervised and Unsupervised Learning