Suppose you are given ‘n’ predictions on test data by ‘n’ different models (M1, M2, …. Mn) respectively. Which of the following method(s) can be used to combine the predictions of these models? Note: We are working on a regression problem 1. Median 2. Product 3. Average 4. Weighted sum 5. Minimum and Maximum 6. Generalized mean rule

1, 3 and 4
1,3 and 6
1,3, 4 and 6
all of above

The correct answer is: D. all of above

The median, average, weighted sum, minimum, maximum, and generalized mean rule are all methods that can be used to combine the predictions of multiple models.

The median is the middle value in a sorted list of numbers. It is a robust measure of central tendency that is not affected by outliers. The average is the sum of a set of numbers divided by the number of numbers in the set. It is a simple and intuitive measure of central tendency, but it can be sensitive to outliers. The weighted sum is the sum of a set of numbers, each of which is multiplied by a weight. The weights can be used to give more importance to some numbers than others. The minimum is the smallest number in a set. The maximum is the largest number in a set. The generalized mean rule is a method for combining the predictions of multiple models that takes into account the uncertainty in the predictions.

In the context of a regression problem, the goal is to predict a continuous value. The median, average, weighted sum, minimum, maximum, and generalized mean rule can all be used to combine the predictions of multiple models to produce a single prediction. The choice of method will depend on the specific application.

For example, if the goal is to produce a robust prediction that is not affected by outliers, then the median may be a good choice. If the goal is to produce a prediction that is as accurate as possible, then the average may be a good choice. If the goal is to produce a prediction that is tailored to the specific application, then the weighted sum or generalized mean rule may be a good choice.