Decision Trees and Ensembling techinques in R studio. Bagging, Random Forest, GBM, AdaBoost & XGBoost in R programming
Sub Category
- Data Science
{inAds}
Objectives
- Solid understanding of decision trees, bagging, Random Forest and Boosting techniques in R studio
- Understand the business scenarios where decision tree models are applicable
- Tune decision tree model's hyperparameters and evaluate its performance.
- Use decision trees to make predictions
- Use R programming language to manipulate data and make statistical computations.
- Implementation of Gradient Boosting, AdaBoost and XGBoost in R programming language
Pre Requisites
- Students will need to install R Studio software but we have a separate lecture to help you install the same
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
{inAds}
Coupon Code(s)