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

**(Select all that apply.)** Which features can be engineered from a timestamp like `2022-12-01 13:14:15`?

(Select all that apply.) Which features can be engineered from a timestamp like <code>2022-12-01 13:14:15</code>?

Which technique is particularly effective for vetting engineered features for decision trees?

Which technique is particularly effective for vetting engineered features for decision trees?

What kind of geometry do the decision boundaries of the decision tree have?

What kind of geometry do the decision boundaries of the decision tree have?

**(True or False)** Using a predictive model to replace missing values is known as imputation.

(True or False) Using a predictive model to replace missing values is known as imputation.

**(True or False)** Imputation is a common source of information leakage.

(True or False) Imputation is a common source of information leakage.

Test what you’ve learned about feature engineering.

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: Feature Engineering