Gain insights into essential machine learning algorithms in R, including CART, random forest, and XGBoost. Discover model tuning and cross-validation to create accurate, robust data science models.

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The R programming language is widely used in the field of data science. Machine learning is a fundamental skill for learners looking to master industry algorithms in the field of data science.

In this course, you’ll learn about several essential algorithms used in machine learning, including classification and regression trees (CART), random forest, and XGBoost. CART is a decision tree algorithm that’s used for both classification and regression problems. Random forest is an ensemble learning method that uses multiple decision trees to improve the accuracy of predictions. XGBoost, short for Extreme Gradient Boosting, is a powerful algorithm that’s also used for regression and classification problems. You’ll also learn about cross-validation and model tuning, which are essential skills for crafting valuable machine learning models. 

After taking this course, you’ll have the crucial skills to ensure that the machine learning models you create are accurate, robust, and reliable.

Data Science with R: Decision Trees and Random Forests

**(True or False)** The XGBoost algorithm adds trees to the ensemble where each tree learns patterns in the errors of the previous tree. 

(True or False) The XGBoost algorithm adds trees to the ensemble where each tree learns patterns in the errors of the previous tree.

**(True or False)** The XGBoost algorithm can only be applied to classification scenarios.

(True or False) The XGBoost algorithm can only be applied to classification scenarios.

Tuning random forest ensembles is simpler.

Tuning both ensembles is equally complicated.

What is the difference between tuning XGBoost ensembles compared to random forest ensembles?

What is the difference between tuning XGBoost ensembles compared to random forest ensembles?

**(True or False)** Random forests typically have better predictive performance than XGBoost.

(True or False) Random forests typically have better predictive performance than XGBoost.

Test what you’ve learned about XGBoost.

Welcome to the Course

Supervised Learning

Classification Tree Math

Using Classification Trees in R

Introducing the Bias-Variance Tradeoff

Model Tuning

Model Tuning with tidymodels

Feature Engineering

Regression Trees

The Random Forest Algorithm

Using Random Forests

Gradient Boosting Trees

Continuing Your Journey

Quiz: XGBoost