what is regression models and how do we measure the performance of model

[manjunath]

Regression models are a type of machine learning model that are used to predict a continuous output value based on one or more input features. They are commonly used in applications such as prediction, forecasting, and trend analysis.
 
There are many different types of regression models, including linear regression, logistic regression, and support vector regression. The specific type of regression model that is most appropriate for a particular problem will depend on the characteristics of the data and the goals of the analysis.
 
To measure the performance of a regression model, there are several metrics that are commonly used. Some common regression evaluation metrics include:
 
·        Mean Absolute Error (MAE): This is the average absolute difference between the predicted values and the actual values.
 
·        Mean Squared Error (MSE): This is the average squared difference between the predicted values and the actual values.
 
·        Root Mean Squared Error (RMSE): This is the square root of the MSE.
 
·        R-squared: This is a measure of how well the model fits the data, with a value of 1 indicating a perfect fit and a value of 0 indicating no fit.
 
*      Adjusted R-squared: This is a modified version of R-squared that takes into account the number of variables in the model