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

Which metric measures a binary classification model's accuracy with positive class labels?

Which metric measures a binary classification model’s accuracy with positive class labels?

When using cross-validation for model tuning, what number of cross-validation folds is considered best practice?

When using cross-validation for model tuning, what number of cross-validation folds is considered best practice?

How many models are trained using ten-fold cross-validation with a single set of hyperparameter values? 

How many models are trained using ten-fold cross-validation with a single set of hyperparameter values?

Assume a tuning grid consists of three different hyperparameters with ten unique values per hyperparameter, how many rows will the tuning grid have to accommodate all combinations?

Assume a tuning grid consists of three different hyperparameters with ten unique values per hyperparameter, how many rows will the tuning grid have to accommodate all combinations?

A data scientist conducts a grid search using ten-fold cross-validation and a tuning grid with 400 rows. How many models will be trained in this grid search?

A data scientist conducts a grid search using ten-fold cross-validation and a tuning grid with 400 rows. How many models will be trained in this grid search?

Test what you’ve learned about model tuning.

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: Model Tuning