MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Random forests deals with the problem of overfitting by creating multiple trees, with each tree trained slightly differently so it overfits differently. Random forests is a classifier that combines a large number of decision trees. The decisions of each tree are then combined to make the final classification.

Detailed explanation-2: -Random forest algorithm avoids and prevents overfitting by using multiple trees. The results are not accurate. This gives accurate and precise results. Decision trees require low computation, thus reducing time to implement and carrying low accuracy.

Detailed explanation-3: -Overfitting happens when a model learns the pattern as well as the noise of the data on which the model is trained. Specifically, the model picks up on patterns that are specific to the observations in the training data but do not generalize to other observations.

Detailed explanation-4: -We can clearly see that the Random Forest model is overfitting when the parameter value is very low (when parameter value < 100), but the model performance quickly rises up and rectifies the issue of overfitting (100 < parameter value < 400).