Can somebody explain to me question 16-17 in the link here ?

Q 16 ) Suppose, you are working on a binary classification problem with 3 input features. And you chose to apply a bagging algorithm(X) on this data. You chose max_features = 2 and the n_estimators =3. Now, Think that each estimators have 70% accuracy.

Note: Algorithm X is aggregating the results of individual estimators based on maximum voting


Hi @sheldon1990,

In question 16, you are asked the maximum possible accuracy that one can achieve using the three models, which can be 100%. To prove the point, author has given an example in which the individual accuracy of M1, M2 and M3 is 70 % ( 7 of 10 correct predictions) while the overall accuracy is 100%.

In question 17, it is asked if the minimum accuracy can be less than 70 or not? Well, it can be, because there are chances that two models predict incorrect value together, and hence while voting, the incorrect prediction will be taken as the final prediction.The same has been shown in the table as a solution.