Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance 2. Generalized models 3. Better interpretability

The correct answer is: C. 1, 2 and 3

Ensemble models are a type of machine learning model that combine the predictions of multiple models to produce a more accurate result. They can be used to improve the performance of machine learning models in a number of ways, including:

• Reducing bias: Ensemble models can reduce bias by averaging the predictions of multiple models, which can help to cancel out any individual model’s biases.
• Reducing variance: Ensemble models can reduce variance by averaging the predictions of multiple models, which can help to make the model’s predictions more stable.
• Improving accuracy: Ensemble models can improve the accuracy of machine learning models by combining the predictions of multiple models. This is because each model in the ensemble can learn different aspects of the data, and by combining the predictions of multiple models, the ensemble can learn more about the data than any individual model can.

In addition to improving performance, ensemble models can also be used to improve the interpretability of machine learning models. This is because ensemble models can be used to generate a set of rules that explain how the model makes its predictions. This can be helpful for understanding how the model works and for identifying any potential biases in the model.

Therefore, the correct answer is: C. 1, 2 and 3.