Adjusted r squared in python for data science
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adjusted r-squared is a modified version of r-squared that penalizes the addition of unnecessary independent variables in a regression model. it provides a more accurate measure of how well the independent variables explain the variation in the dependent variable.
the formula for adjusted r-squared is:
adjusted r-squared = 1 - (1 - r-squared) * (n - 1) / (n - k - 1)
where:
r-squared is the coefficient of determination
n is the number of observations
k is the number of independent variables
in python, you can calculate adjusted r-squared using the `statsmodels` library, which provides a comprehensive set of tools for statistical analysis. here's an example code snippet that demonstrates how to calculate adjusted r-squared for a linear regression model:
in this example, we first generate some sample data with two independent variables and a dependent variable. we then fit a linear regression model using `statsmodels.ols` and calculate the r-squared and adjusted r-squared values.
by using adjusted r-squared, you can better evaluate the goodness of fit of your regression model, especially when working with multiple independent variables.
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