My proposed solution to calculating metrics for LOOCV

001-LOOCV-r2-MSE

loo = LeaveOneOut()
ytests = []
ypreds = []
for train_idx, test_idx in loo.split(Xr):
    X_train, X_test = X_array[train_idx], X_array[test_idx] #requires arrays
    y_train, y_test = y_array[train_idx], y_array[test_idx]
    
    model = LinearRegression()
    model.fit(X = X_train, y = y_train) 
    y_pred = model.predict(X_test)
        
    # there is only one y-test and y-pred per iteration over the loo.split, 
    # so to get a proper graph, we append them to respective lists.
        
    ytests += list(y_test)
    ypreds += list(y_pred)
        
rr = metrics.r2_score(ytests, ypreds)
ms_error = metrics.mean_squared_error(ytests, ypreds)
        
print("Leave One Out Cross Validation")
print("R^2: {:.5f}%, MSE: {:.5f}".format(rr*100, ms_error))