Data Science with R: Decision Trees and Random Forests

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.

Intermediate

65 Lessons

14h

Certificate of Completion

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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Explanations

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This course includes

67 Playgrounds

11 Quizzes

This course includes

67 Playgrounds

11 Quizzes

Course Overview

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 u...Show More

TAKEAWAY SKILLS

Data Science

Machine Learning

Machine Learning Fundamentals

What You'll Learn

An understanding of the basics of machine learning and supervised learning

Familiarity with the differences between classification and regression trees

A working knowledge of XGBoost algorithm

Familiarity with random forest

What You'll Learn

An understanding of the basics of machine learning and supervised learning

Course Content

Welcome to the Course

Get familiar with machine learning fundamentals, key datasets, and predictive analytics in R.

Course Expectations

Machine Learning Is Predictive Analytics

The Course Datasets Quiz: Data Basics

Supervised Learning

Unpack the core of supervised learning, decision trees, overfitting, and model tuning in machine learning.

What Is Machine Learning?

Forms of Supervised Learning Why Decision Trees?Decision Tree Intuition Overfitting Intuition Model Tuning Intuition Quiz: Machine Learning Concepts

Classification Tree Math

Examine key mathematical concepts and techniques for optimizing classification tree splits using Gini impurity.

Gini Impurity Gini Change Classification Tree Training Example Many Categories Impurity Numeric Feature Impurity Quiz: Classification Tree Math

Using Classification Trees in R

Apply your skills to conduct EDA, prepare data, specify algorithms, and fit classification trees in R.

Exploratory Data Analysis—Profiling Exploratory Data Analysis: Visualization Introducing tidymodels Crafting the Recipe Specify the Algorithm and Workflow Fitting the Model Quiz: Fitting Models

Introducing the Bias-Variance Tradeoff

Take a look at understanding model complexity and the bias-variance tradeoff in machine learning.

Underfitting and Overfitting The Bias-Variance Tradeoff Quiz: Underfitting, Overfitting, and the Bias-Variance Tradeoff

Model Tuning

4 Lessons

Tackle cross-validation, optimal model tuning, decision tree complexity, and pruning to enhance model performance.

Model Tuning with tidymodels

5 Lessons

Piece together the parts of model accuracy, cross-validation, hyperparameter tuning, and visualization with tidymodels.

Feature Engineering

6 Lessons

Step through transforming raw data into meaningful features, avoiding information leakage, creating decision boundaries, and handling missing data.

Regression Trees

5 Lessons

Walk through regression trees, SSE, and training with tidymodels for numeric predictions.

10.

The Random Forest Algorithm

4 Lessons

Break apart the random forest's ensemble, bagging, and feature randomization techniques.

11.

Using Random Forests

7 Lessons

Break down the steps to train, tune, and evaluate random forest models using R.

12.

Gradient Boosting Trees

6 Lessons

Map out the steps for implementing and tuning Gradient Boosting Trees using XGBoost in R.

13.

Continuing Your Journey

1 Lesson

Focus on hands-on practice, engaging in projects, and expanding skills to unsupervised learning.

Credit Card Fraud Detection using the R Language

Project

Course Author

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Anthony Walker

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Emma Bostian 🐞

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Evan Dunbar

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Carlos Matias La Borde

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Front-end Developer

Vinay Krishnaiah

Software Developer

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Anthony Walker

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Emma Bostian 🐞

@EmmaBostian

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