10 Most Common Machine Learning Algorithms Explained -2023

1. Linear Regression Linear regression is a statistical method used to examine the relationship between two continuous variables: one independent variable and one dependent variable. The goal of linear regression is to find the best-fitting line through a set of data points, which can then be used to make predictions about future observations. <img alt="Linear Regression" src="https://miro.medium.com/v2/resize:fit:700/0*TZaDL3pAQBvIK8QS.png" style="height:550px; width:700px" /> The equation for a simple linear regression model is: y = b0 + b1*x where y is the dependent variable, x is the independent variable, b0 is the y-intercept (the point at which the line crosses the y-axis), and b1 is the slope of the line. The slope represents the change in y for a given change in x. To determine the best-fitting line, we use the method of least squares, which finds the line that minimizes the sum of the squared differences between the predicted y values and the actual y values. Linear regression can also be extended to multiple independent variables, known as multiple linear regression. The equation for a multiple linear regression model is: <a href="https://medium.com/@riteshgupta.ai/10-most-common-machine-learning-algorithms-explained-2023-d7cfe41c2616">Visit Now</a>