Question

Which of the following statement(s) is/are correct for boosting technique?

a) weight of observation, to be selected while building the next weak learner, increases if the observation is correctly classified
b) weight of observation, to be selected while building the next weak learner, increases, if the observation is incorrectly classified
c) weight of observation, to be selected while building the next weak learner, decreases if the observation is correctly classified
d) weight of observation, to be selected while building the next weak learner, decreases if the observation is incorrectly classified

Answer 1

The correct statements for boosting technique are: b) weight of observation, to be selected while building the next weak learner, increases, if the observation is incorrectly classified and c) weight of observation, to be selected while building the next weak learner, decreases if the observation is correctly classified.

Step-by-step explanation:

The correct statement(s) for boosting technique are:

b) Weight of observation increases if the observation is incorrectly classified.

c) Weight of observation decreases if the observation is correctly classified.

Boosting is a machine learning ensemble method that combines weak learners to create a strong learner. In boosting, observations that are incorrectly classified by the previous weak learner are given higher weights, while observations that are correctly classified are given lower weights.

This approach allows the subsequent weak learners to focus more on the observations that are harder to classify, improving the overall performance of the ensemble model.