What are the main differences between supervised learning and unsupervised learning within the confines of machine learning? Explain your answer in 3-5 sentences that are much shorter

Answer 1

Supervised learning involves training a model with labeled data, where the algorithm is provided with input-output pairs to learn from. In unsupervised learning, the algorithm is given only input data with no corresponding output labels, and it is tasked with finding patterns or relationships within the data on its own. Supervised learning is used for tasks like classification and regression, while unsupervised learning is used for clustering, dimensionality reduction, and anomaly detection. Overall, supervised learning requires more data preprocessing and labeling, while unsupervised learning is more exploratory and can uncover hidden patterns in data.