[FIXED] How to calculated the adjusted R2 value using scikit

Issue

I have a dataset for which I have to develop various models and compute the adjusted R2 value of all models.

    cv = KFold(n_splits=5,shuffle=True,random_state=45)
    r2 = make_scorer(r2_score)
    r2_val_score = cross_val_score(clf, x, y, cv=cv,scoring=r2)
    scores=[r2_val_score.mean()]
    return scores

I have used the above code to calculate the R2 value of every model. But I am more interested to know the adjusted R2 value of every models Is there any package in python which can do the job?

I will appreciate your help.

Solution

you can calculate the adjusted R2 from R2 with a simple formula given here.

Adj r2 = 1-(1-R2)*(n-1)/(n-p-1)

Where n is the sample size and p is the number of independent variables.

Adjusted R2 requires number of independent variables as well. That's why it will not be calculated using this function.

Answered By - min2bro