R-squared score is a statistical measure used to determine the goodness of fit of a regression model. It is a crucial metric that helps researchers and data scientists to evaluate the accuracy of their models. The R-squared score ranges from 0 to 1, with higher values indicating a better fit between the model and the data.
In simple terms, the R-squared score measures the proportion of variability in the dependent variable that is explained by the independent variables in the model. This means that the closer the R-squared score is to 1, the better the model is at predicting the dependent variable. However, it is important to note that a high R-squared score does not necessarily mean that the model is perfect, as there may still be other factors that affect the dependent variable that are not accounted for in the model.
Evaluating the R-squared score is an essential step in regression analysis, as it helps researchers to determine whether their model is suitable for the data at hand. In this article, we will explore the importance of R-squared score in evaluating regression model fit, and provide insights into how to interpret and use this measure to improve the accuracy of your models.
What is R-squared Score?
Definition
R-squared score, also known as the coefficient of determination, is a statistical measure that evaluates the goodness of fit of a regression model. It is a value between 0 and 1 that represents the proportion of variance in the dependent variable that is explained by the independent variable(s) in the model.
In other words, R-squared score measures how well the regression model fits the observed data. A higher R-squared score indicates a better fit of the model to the data, while a lower R-squared score indicates a weaker fit.
Read more about R-squared Score: A Comprehensive Guide to Evaluating Regression Model Fit here
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