The correct answer is C. Averaging ensemble can be used in both classification as well as regression.
Averaging ensemble is a machine learning technique that combines the predictions of multiple models to produce a single, more accurate prediction. This can be done by averaging the predictions of the models, or by taking the majority vote of the models.
Averaging ensemble can be used in both classification and regression problems. In classification, the goal is to predict the class label of an input data point. In regression, the goal is to predict a continuous value for an input data point.
Averaging ensemble can improve the accuracy of machine learning models by reducing the variance of the predictions. This is because the predictions of the individual models are likely to be different, and averaging them will reduce the impact of any individual model’s error.
Averaging ensemble is a simple and effective machine learning technique that can be used to improve the accuracy of a variety of machine learning models.
Option A is incorrect because averaging ensemble can be used in both classification and regression problems.
Option B is incorrect because averaging ensemble can be used in both classification and regression problems.
Option D is incorrect because averaging ensemble is a valid machine learning technique.