Supervised and unsupervised learning are two broad categories of machine learning algorithms.
Supervised learning involves a training process in which the machine learning model is provided with a labeled dataset, i.e., a dataset with input features and corresponding output labels. The model then learns to predict the output labels for new input data based on the patterns found in the labeled dataset. In simpler words, the model is trained to map input features to their corresponding output labels. Some popular examples of supervised learning algorithms are linear regression, decision trees, and support vector machines (SVMs).
On the other hand, unsupervised learning involves a training process in which the machine learning model is provided with an unlabeled dataset, i.e., a dataset with input features but no corresponding output labels. The model then learns to identify patterns or groupings in the input data without any guidance or supervision. In simpler words, the model tries to find the underlying structure of the data without knowing what the data represents. Some popular examples of unsupervised learning algorithms are clustering (e.g., K-means clustering) and dimensionality reduction (e.g., principal component analysis or PCA).
To understand the difference between supervised and unsupervised learning, let’s take an example of text classification. In supervised learning, the model is provided with a labeled dataset of text documents, where each document is assigned to a certain category, say sports or politics. The model can then learn to predict the category of a new text document by analyzing its features, such as the words used, sentence structure, and topic. In contrast, in unsupervised learning, the model might be given an unlabeled dataset of text documents and asked to cluster them into groups based on similarities without knowing beforehand what categories the documents belong to.
In summary, supervised learning is used when we have labeled data and want the model to learn a mapping between input features and output labels. In contrast, unsupervised learning is used when we have unlabeled data and want the model to discover the underlying structure or patterns in the data without any prior knowledge of the output labels.