A high R-squared score in linear regression shows that the model and data are well-fitted. An R-squared value greater than 0.7 is regarded good, indicating that the model explains 70% or more of the variability in the response variable. A low R-squared value, usually less than 0.3, indicates a poor fit, implying that the model does not explain a significant portion of the variability. However, the definition of a “good” or “bad” R-squared number varies depending on the field and context. For example, in the social sciences, lower R-squared values are more prevalent and may still be regarded appropriate. for Demo Class contact today at Durga Online Trainer
