Great, you’re sorted with Machine Learning and Artificial Intelligence and you’re good to go, right? Wrong. Now you need to think about data collection, algorithms and all the rest of it! Don’t worry, we’re here to help you out! In Machine Learning (ML) and Artificial Intelligence (AI), both of which we have covered in previous blogs, there are two main ways that an algorithm can make observations in a dataset, that being through supervised or unsupervised learning. Today we’ll have a look at what the differences are between the two, as well as which would be better for YOU. Let’s get to it.
What is Supervised Learning?
Supervised learning occurs when a model is trained using data that already exists (historical data). Supervised learning uses a labelled dataset, where each observation in the dataset already has a meaningful tag associated with it and the dataset possesses column headings, also known as features or attributes. A dataset in supervised learning can possess either continuous or categorical target features.
Continuous target features are numerical values and are a result of regression models. Categorical target features are non-numeric values that belong to different classes. Categorical target features are the result of classification models that predict discrete categorical labels. So basically, Supervised learning models try to determine the most appropriate functional mapping between the input features and target feature for a given sample dataset. Some supervised learning algorithms include logistic regression, support vector machines, artificial neural networks and random forests.
What is Unsupervised Learning?
Unsupervised learning occurs when an ML model is not trained on an existing sample dataset but rather the model trains itself on data that it has not seen. The ML model is then able to discover trends, find patterns and make observations on an unlabelled dataset, where each observation does not possess meaningful tags. The algorithms that are used for unsupervised learning are difficult to understand and use, since little to no information is given about the data or what the expected outcomes could be. Some of these algorithms include, feature selection and clustering. Feature selection is used to decrease the number of redundant features in a dataset. Clustering is used to group observations that are similar and can be used to summarise the data as well as perform anomaly detection. Basically, in unsupervised learning, models attempt to find the most natural mapping between the input features and target features of a dataset without any prior expertise or information.
There are various key differences between supervised and unsupervised learning:
The first key difference is that supervised learning requires more human intervention than unsupervised learning since the dataset has to be labelled appropriately. However, with unsupervised learning the model works independently to find a suitable structure although human intervention is somewhat required to validate the outputs of the model.
The second key difference is that the goal of supervised learning is to predict outcomes on new data in which the results are known, whereas the goal for unsupervised learning is to investigate large volumes of data and make observations that are beyond the capabilities of humans.
The third key difference between supervised and unsupervised learning is the required computational expense. For supervised learning, simple tools can be used however, the process to train the model on an existing dataset and store it, is time-consuming and can result in additional expenses. Supervised learning may require expert knowledge to label data in a dataset, therefore it can become expensive to source experts in a particular field to work as data annotators. For unsupervised learning, more powerful tools are required to process the dataset which can result in increased costs. Since no training is required, the computational expense decreases but human intervention is still necessary to validate outputs. Unsupervised learning requires a larger dataset for insights to be made and preprocessing is required which contributes to the computational complexity of the learning model.
When choosing whether to use supervised or unsupervised learning on your data, it is extremely important to consider which model most appropriately fits the dataset that you have. Supervised learning is favoured when you have a labelled dataset or when experts can be easily consulted to annotate your data, and when you have a well-defined problem. Unsupervised learning is advantageous when you want to find new problems and make new observations from your dataset, having an unlabelled dataset. However, you need to always ensure that your dataset supports the algorithms that you choose so that your ML models can be as effective as possible.
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