Here you will find the answers of “**Which of the following is true about bias and variance?**“. This question is a part ” **Machine Learning With R** ”

Question: When building a decision tree, we want to split the nodes in a way that decreases entropy and increases information gain.

- Having a high bias underfits the data and produces a model that is overly complex, while having high variance overfits the data and produces a model that is overly generalized.
**Having a high bias underfits the data and produces a model that is overly generalized, while having high variance overfits the data and produces a model that is overly complex.**- Having a high bias overfits the data and produces a model that is overly complex, while having high variance underfits the data and produces a model that is overly generalized.
- Having a high bias overfits the data and produces a model that is overly generalized, while having high variance underfits the data and produces a model that is overly complex.

Correct Answer: 2

#### About **Machine Learning With R**

This Machine Learning with R course dives into the basics of machine learning using an approachable, and well-known, programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.

Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!

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