<p><a target="_blank" href="https://www.almabetter.com/bytes/tutorials/data-science/regression-in-machine-learning">Regression in machine learning</a> is a supervised learning technique used to predict continuous numeric outcomes based on input features. It models the relationship between independent variables (features) and dependent variables (target) using a mathematical function. Common regression algorithms include linear regression, polynomial regression, and regression trees. The goal is to find the best-fitting line or curve that minimizes the difference between predicted and actual values. Regression analysis provides insights into the relationship between variables, aids in forecasting, and is widely applied in areas such as finance, economics, healthcare, and engineering for tasks like predicting sales, estimating prices, and analyzing trends.</p>


[Regression in machine learning](https://www.almabetter.com/bytes/tutorials/data-science/regression-in-machine-learning) is a supervised learning technique used to predict continuous numeric outcomes based on input features. It models the relationship between independent variables (features) and dependent variables (target) using a mathematical function. Common regression algorithms include linear regression, polynomial regression, and regression trees. The goal is to find the best-fitting line or curve that minimizes the difference between predicted and actual values. Regression analysis provides insights into the relationship between variables, aids in forecasting, and is widely applied in areas such as finance, economics, healthcare, and engineering for tasks like predicting sales, estimating prices, and analyzing trends.

Regression in Machine Learning