![]() ![]() In general, models with higher R-squared values are preferred because it means the set of predictor variables in the model is capable of explaining the variation in the response variable well. If we’d like, we could then compare this R-squared value to another regression model with a different set of predictor variables. There is usually something you can do for yourself: calculate the correlation between the observed response and the predicted response and then square it. This means that 71.76% of the variation in the exam scores can be explained by the number of hours studied and the number of prep exams taken. The R-squared of the model turns out to be 0.7176. We can use the LinearRegression() function from sklearn to fit a regression model and the score() function to calculate the R-squared value for the model: from sklearn.linear_model import LinearRegression ![]() Suppose we have the following pandas DataFrame: import pandas as pdĭf = pd. The following example shows how to calculate R 2 for a regression model in Python. 1 indicates that the response variable can be perfectly explained without error by the predictor variables.0 indicates that the response variable cannot be explained by the predictor variable at all.The value for R-squared can range from 0 to 1 where: R-squared, often written R 2, is the proportion of the variance in the response variable that can be explained by the predictor variables in a linear regression model. ![]()
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