Which of the following terms is used to describe this combination of models?

A data scientist has produced two models for a single machine learning problem. One of the models performs well when one of the features has a value of less than 5, and the other model performs well when the value of that feature is greater than or equal to 5. The data scientist decides to combine the two models into a single machine learning solution.

Which of the following terms is used to describe this combination of models?
A . Bootstrap aggregation
B . Support vector machines
C . Bucketing
D . Ensemble learning
E . Stacking

View Answer

Answer: D

Explanation:

Ensemble learning is a machine learning technique that involves combining several models to solve a particular problem. The scenario described fits the concept of ensemble learning, where two models, each performing well under different conditions, are combined to create a more robust model. This approach often leads to better performance as it combines the strengths of multiple models.

Reference

Introduction to Ensemble Learning: https://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/