What is use of scoring parameter in k fold cross validation?

#1

I am not able to understand that what is use of ‘scoring’ in this…

#2

Hi, the use of scoring in cross_validation is which score you want to compute. Like in logistic classification task we used accuracy to tell how good our algo is. If the prediction matches then our accuracy is improving. It was like { np.sum(Y_pred==y_true)}. But in linear regression calculating the accuracy is of no significance. For eg for a point x1 if the y1=2.5 and our linear regression algo predicted 2.49, then in the accuracy it will not be counted as 2.5 is not equal to 2.49. But as it is close enough, and in linear regression we want to compute close enough estimated values therefore we used the coefficient of determination. So as algo and problems vary our metrics or improving algo criteria also changes For one algo we are monitoring accuracy and for another we are monitoring r2(coeff of determination) score. This is what given in as input to the scoring param in cross_validation

#3

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