Understanding the Difference Between Supervised and Unsupervised Learning

1. What They Are:

   • Supervised Learning: This uses labeled data. That means each piece of input has a known output. About 70-80% of machine learning projects use this method.
   • Unsupervised Learning: This uses unlabeled data. Here, the goal is to find patterns or groups without any help. Only about 20-30% of projects use this type.

2. What They Aim To Do:

   • Supervised Learning: The main goal is to predict results based on new information. This type is often used for sorting things into categories (classification) or for finding numbers in a range (regression).
   • Unsupervised Learning: The focus here is to uncover hidden patterns or natural groupings. This is usually done for grouping similar items together (clustering) or finding relationships between them (association).

3. The Techniques They Use:

   • Supervised Learning: Some common techniques are Decision Trees, SVM (Support Vector Machines), and Neural Networks.
   • Unsupervised Learning: Popular techniques include K-Means, Hierarchical Clustering, and PCA (Principal Component Analysis).

4. How We Measure Success:

   • Supervised Learning: We look at things like Accuracy and F1 Score to measure how well it works.
   • Unsupervised Learning: We check results using Silhouette Score and the Davies-Bouldin Index to see how well the patterns are formed.