Adjusted R Squared, Clearly Explained

Looking to learn about Adjusted R Squared? Adjusted R-squared is a statistical measure that compares the fit of a model to the average of all possible models. It is a modified version of R-squared that accounts for the number of predictors in the model. A higher adjusted R-squared indicates a better fit of the model to the data. It also penalizes the addition of unnecessary variables to the model. Adjusted R-squared values can range from 0 to 1, with values closer to 1 indicating a better fit. Learn more in this video!
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[@sds-superdatascience]

Thank you for subscribing and following our videos. You can find the course HERE: sds.courses/python-ml-level-1

[@jessebrn30]

well done, explained very clear!

[@nikhilsingh1296]

I am not able to Understand Adjusted RSquare, can someone help.

[@bin4ry_d3struct0r]

n - k - 1 is called the degrees of freedom of the linear equation, correct?

[@kdmpfortniteshorts8962]

Ye😊

[@raphaeld.s.1933]

Greater R^2 values are NOT necessarily better

[@hnevko]

what do you mean by variable? a variable, the variable? or just numbers, or observations? So many fkin terminology, words, words! Anyway, so you have the A (Y axis) and B (X axis) variables.By adding a variable you mean adding C, so multinomial? If I have 2 variables, adjusted R2 should be the same as normal one? Its not.