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)** CART regression trees predict numeric values.

(True or False) CART regression trees predict numeric values.

**(True or False)** CART regression trees use the Gini impurity calculation.

(True or False) CART regression trees use the Gini impurity calculation.

**(True or False)** CART regression trees use the average of leaf nodes for predictions.

(True or False) CART regression trees use the average of leaf nodes for predictions.

Sorting the categorical values alphanumerically 

Sorting the feature to be predicted in ascending order

Calculating the mean of the feature to be predicted for each categorical level

Sorting the feature to be predicted in ascending order for each categorical level

What is the first step in splitting a categorical feature in a regression tree?

What is the first step in splitting a categorical feature in a regression tree?

Test what you’ve learned about regression trees.

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: Regression Trees