In machine learning, regression refers to mathematical techniques that allow data scientists to forecast a continuous outcome (y) based on the values of one or more predictor variables (x). Because of its ease of application in predicting and forecasting, linear regression is perhaps the most popular type of regression analysis.
Multiple regression is a statistical method for examining the relationship between numerous independent variables and a single dependent variable. The goal of multiple regression analysis is to predict the value of a single dependent variable by using known independent variables.
The two types of regression analysis approaches utilised to tackle the regression problem using machine learning are linear regression and logistic regression. They are the most often used regression approaches.
When anticipating the likelihood of a given result, such as whether or not a customer would churn in 30 days, "prediction" refers to the output of an algorithm after it has been trained on a previous dataset and applied to new data.
Companies that are developing cutting-edge technologies for generating machine-learning models as well as gathering and managing the massive amounts of data required to train those models are among the 12 trendiest machine-learning startups.
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