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

Permuting important features has no impact on predictive performance.

Permuting important features decreases predictive performance.

Permuting unimportant features has little impact on predictive performance.

Permuting unimportant features impacts predictive performance.

**(Select all that apply.)** What’s the key to understanding the importance of permuting features?

(Select all that apply.) What’s the key to understanding the importance of permuting features?

**(True or False)** The random forest algorithm automatically optimizes for the bias-variance tradeoff.

(True or False) The random forest algorithm automatically optimizes for the bias-variance tradeoff.

**(True or False)** Random forests are always trained with 500 decision trees.

(True or False) Random forests are always trained with 500 decision trees.

Before the entire data is split into training and test datasets

Right after the entire data is split into training and test datasets

When should the test data be transformed to match the training data used to train a model?

When should the test data be transformed to match the training data used to train a model?

The OOB estimates tend to be accurate but optimistic.

The OOB estimates tend to be accurate but pessimistic.

Neither OOB nor test set estimates are accurate.

What is a typical behavior when the program compares random forest out-of-bag (OOB) estimates to test set estimates?

What is a typical behavior when the program compares random forest out-of-bag (OOB) estimates to test set estimates?

Test what you’ve learned about using random forests.

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: Using Random Forests