Standard Error of the Regression vs. R-squared
Characterists | R Squared | Standard Error |
|---|---|---|
Objective | The objective of R squared is to determine how much is the strength of relationship between Independent and Dependent variables | The objective of this metric is to find the average distance between actual values and the predicted values through which the regression line passes. |
Measurement Focus | R-Squared lies between 0 and 1. | Standard error does not lie between 0 and 1. |
Purpose and use | R2-squared is used for comparison between two models. | Helps to determine the precision of predictions |
Interpretation | Higher the value more is the variability between dependent and independent variable. | Lower the value better is the model. |
Relationship Between the Two Metrics | There is no formula based relationship but as one decreases the other one increases and vice versa | There is no formula based relationship but as one decreases the other one increases and vice versa |
Standard Error of the Regression vs. R-squared
Regression is a statistical technique used to establish a relationship between dependent and independent variables. It predicts a continuous set of values in a given range. The general equation of Regression is given by
- Here y is the dependent variable. It is the variable whose value changes when the independent values are changed
- x is the independent variable. Here y is dependent on x. It is to be noted that there can be more than one independent variable.
- m is the slope
- c is the y-intercept
There are different types of Regression: Linear Regression, Ridge Regression, Polynomial Regression, and Lasso Regression. Regression analysis involves the prediction of continuous values within a given range therefore we require evaluation metrics. Evaluation metrics help us to analyze the performance of the Machine Learning model. In Regression Analysis, we calculate how much the predicted values deviate from the actual values. There are different evaluation metrics for Regression Analysis like Mean Squared Error, Mean Absolute Error, R squared, etc.