{"id":266809,"date":"2024-11-13T10:17:31","date_gmt":"2024-11-13T10:17:31","guid":{"rendered":"https:\/\/imarticus.org\/blog\/?p=266809"},"modified":"2024-11-13T10:17:31","modified_gmt":"2024-11-13T10:17:31","slug":"regression-vs-classification","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/regression-vs-classification\/","title":{"rendered":"Regression vs. Classification Techniques for Machine Learning"},"content":{"rendered":"

Machine learning (ML), a subset of Artificial Intelligence, empowers computers to learn from data and make intelligent decisions without explicit programming.<\/span><\/p>\n

Regression and classification are two essential techniques within the ML domain, each with a unique purpose and application. Let’s learn about the differences between regression vs classification, when to use them, and their distinct applications.<\/span><\/p>\n

If you want to learn how to use regression and classification techniques for machine learning, you can enrol in Imarticus Learning\u2019s 360-degree <\/span>data analytics course<\/b><\/a>.<\/span><\/p>\n

Understanding the Basics<\/span><\/h2>\n

Before delving into <\/span>regression vs classification<\/span>, grasping the core concept of supervised learning techniques is essential. In supervised learning, an algorithm is trained on a labelled dataset, where each data point is associated with a corresponding output. The algorithm in <\/span>supervised learning techniques<\/span> learns to map input features to output labels, enabling it to make predictions on unseen data.<\/span><\/p>\n

Regression Analysis: Predicting Continuous Values<\/span><\/h2>\n

Regression analysis is a statistical method for modeling the relationship between a dependent variable and one or more independent variables. In ML, regression techniques are employed to predict continuous numerical values.<\/span><\/p>\n

Types of Regression<\/span><\/h3>\n
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  1. Linear Regression:<\/b> This is the simplest form of regression, where a linear relationship is assumed between the independent and dependent variables.<\/span><\/li>\n
  2. Polynomial Regression:<\/b> This technique allows for modelling complex, non-linear relationships by fitting polynomial curves to the data.<\/span><\/li>\n
  3. Logistic Regression:<\/b> Despite its name, logistic regression is a classification technique used to predict the probability of a binary outcome. However, it can be adapted for regression tasks by predicting continuous values within a specific range.<\/span><\/li>\n<\/ol>\n

    Applications of Regression<\/span><\/h3>\n