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

At the beginning of the project before training a model

In the middle of the project after a few modeling iterations have been completed

At the end of the project after the final model has been trained

Data needs to be split many times throughout the project.

At what point in a machine learning project is the data split into training and test sets?

At what point in a machine learning project is the data split into training and test sets?

The model has optimal complexity for the test set.

A CART decision tree is trained on a large training set. When the model is examined, the decision tree is relatively small. In this scenario, what’s the most likely outcome?

A CART decision tree is trained on a large training set. When the model is examined, the decision tree is relatively small. In this scenario, what’s the most likely outcome?

Bob’s dart throwing in the competition would exhibit high bias and low variance.

Bob’s dart throwing in the competition would exhibit high bias and high variance.

Bob’s dart throwing in the competition would exhibit low bias and high variance.

Bob’s dart throwing in the competition would exhibit low bias and low variance.

Categorize the following scenario: Bob has adjusted his dartboard at the regulation height, but he hasn’t been practicing with random noises. Bob’s next competition will be in a busy (i.e., noisy) pub.

Categorize the following scenario: Bob has adjusted his dartboard at the regulation height, but he hasn’t been practicing with random noises. Bob’s next competition will be in a busy (i.e., noisy) pub.

The algorithm's hyperparameters are set incorrectly.

**(Select all that apply.)** When a machine learning model exhibits high bias and high variance, what is a likely cause? 

(Select all that apply.) When a machine learning model exhibits high bias and high variance, what is a likely cause?

Test what you’ve learned about underfitting, overfitting, and the bias-variance tradeoff.

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: Underfitting, Overfitting, and the Bias-Variance Tradeoff