Supervised Learning vs. Unsupervised Learning in Data Science

In the context of data science, supervised learning and unsupervised learning are two fundamental approaches to machine learning.

Supervised Learning

In supervised learning, the algorithm is trained on a labeled dataset, where the input data is paired with the corresponding output labels. The goal of supervised learning is to learn a mapping function from input to output so that the algorithm can make predictions on unseen data.

Unsupervised Learning

On the other hand, unsupervised learning involves training the algorithm on an unlabeled dataset, where there are no predefined output labels. The algorithm learns the underlying structure or patterns in the data without explicit guidance.

One key difference between supervised and unsupervised learning is the presence or absence of labeled data, which significantly impacts the training process and the types of problems that can be addressed.

In summary, supervised learning requires labeled data for training, while unsupervised learning operates on unlabeled data to discover patterns or relationships within the data.