**December 29, 2020**

Classification problems are one of the two sub groups of supervised learning, other being regression problems. Target variable in classification problems, unlike regression, is a categorical variable. In this we don’t try to predict the actual value of the target variable but try to assign it an appropriate category.

Classification algorithms try to draw a curve that separates data points in a manner that all similar points are stacked together. Often classification is confused with clustering but they are miles apart. Following are the differences between classification and clustering problems.

- Supervised vs Unsupervised: Classification problems fall under supervised learning, while clustering falls under unsupervised learning.
- Pre-defined number of Categories: In classification problems we have a predefined set of categories and they are unaffected by the choice of algorithm. On the other hand in clustering problems, the algorithm has to figure out the number of categories as well as what they are. The number of categories in clustering problems may change with the choice of algorithm.
- Anomalies: It is entirely possible in clustering problems that a new data point might not belong to any of the existing categories but not in classification problem.

by : Monis Khan

**Quick Summary**:

Classification problems are one of the two sub groups of supervised learning, other being regression problems. Target variable in classification problems, unlike regression, is a categorical variable. In this we don’t try to predict the actual value of the target variable but try to assign it an appropriate category. Classification algorithms try to draw a […]