Data Science with R: Decision Trees and Random Forests
This course introduces classification and regression trees (CART), random forest, and XGBoost machine learning algorithms using the R programming language.
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.
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