Regression vs Classification in Machine Learning #ai #machinelearning #regression #classification

24 May 2025 · 1 min

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1:06

In this lecture-style YouTube video, the speaker introduces the two foundational pillars of supervised machine learning: regression and classification. Regression is defined as the task of predicting continuous numeric values, with examples such as house price estimation, sales forecasting, and credit risk assessment. The speaker lists common regression algorithms, including linear regression, ridge and lasso regression, and support vector machines for regression. Classification is presented as the task of predicting discrete class labels, illustrated by spam detection, image recognition, medical diagnosis, and sentiment analysis. Typical classification algorithms mentioned are logistic regression, Naive Bayes, decision trees, k‑nearest neighbors, and support vector machines. The video concludes by noting that neural networks are versatile models capable of handling both regression and classification problems.

Chapters

In machine learning, especially in supervised learning, 2 techniques are fundamental pillars, namely regression and classification. In regression, the objective is to predict a continuous value as a target. This is useful when we want to predict the price of a house based on features such as surface area, number of rooms, location, and so on. Regression is also important for sales forecasting, bank credit risk assessment, etc. The best-known algorithms are linear regression, ridge and lasso regression, and support vector machine for regression. On the other hand, classification aims to predict a class, which is a discrete value. This is also called a qualitative value. Examples include predicting whether an email is spam or not, image recognition, medical diagnosis, sentiment analysis, etc. The best-known algorithms are logistic regression, Naive Bayes, decision tree, k-nearest neighbors, and SVM. Neural networks are suited to both regression and classification problems.