Understanding Linear Regression: A Foundation of Machine Learning
Linear Regression is a fundamental algorithm in Machine Learning used for predicting continuous numerical values based on input variables. It analyzes historical data to find the best-fitting line that minimizes prediction errors. While it is easy to understand and fast to train, it has limitations such as sensitivity to outliers and assumptions of linear relationships.
- ▪Linear Regression is used to predict values like house prices and sales revenue based on historical data.
- ▪There are two main types of Linear Regression: Simple and Multiple, with the latter using multiple input features for more accurate predictions.
- ▪The model's performance can be evaluated using metrics like Mean Absolute Error, Mean Squared Error, and R-Squared.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3734908) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Tarun Kumar Posted on Jun 3 Understanding Linear Regression: A Foundation of Machine Learning #webdev #programming #ai #machinelearning Linear Regression is one of the most fundamental and widely used algorithms in Machine Learning and Statistics. It helps us understand relationships between variables and make predictions based on historical data.
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