R-Squared (R²) is a statistical concept used in regression models and correlation analysis. It provides a measure of how well data points fit a statistical model or line. The R² coefficient is a number between 0 and 1, with 0 indicating that the model does not explain any of the variance in the data, and 1 indicating that the model explains all of the variance in the data.
In a regression analysis, R² is used to determine the extent to which a dependent variable (the variable that is being predicted) can be predicted by one or more independent variables (the variables used in the model). An R² of 1 means that the model explains 100% of the variance in the dependent variable; the lower the R², the less of the variance the model explains.
In machine learning, R² is often used in assessing how well classification or regression models fit a given data set. The higher the R², the more variance is explained.
R² is also used in correlation analyses, where it measures the degree of linear association between two variables. A high R² indicates a strong correlation between the variables, while a low R² indicates a weak correlation.
R² can be a useful tool for understanding data and evaluating the accuracy of predictions or models, with a higher R² generally indicating a more reliable prediction.
