HomeCoursesData Science with R: Decision Trees and Random Forests

Intermediate

14h

Updated 4 months ago

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.

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Overview

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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.

The R programming language is widely used in the field of data science. Machine learning is a fundamental skill for learners loo...Show More

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

An understanding of the basics of machine learning and supervised learning

TAKEAWAY SKILLS

Data Science

Machine Learning

Machine Learning Fundamentals

Content

65 Lessons11 Quizzes

Welcome to the Course

4 Lessons

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

7 Lessons

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

6 Lessons

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

7 Lessons

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

3 Lessons

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.

Cross-Validation

Model Tuning Intuition 201

Controlling Complexity

Pruning Classification Trees

Model Tuning with tidymodels

5 Lessons

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

Measuring Model Accuracy

Performing Cross-Validation

Tuning a Classification Tree

Visualizing a Tuned Classification Tree

Quiz: Model Tuning

Feature Engineering

6 Lessons

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

Feature Engineering Intuition

Information Leakage

Decision Boundaries

Engineering Features for Decision Trees

Missing Data

Quiz: Feature Engineering

Regression Trees

5 Lessons

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

Regression Tree Basics

Numeric Feature SSE

Many Categories SSE

Regression Trees with tidymodels

Quiz: Regression Trees

10.

The Random Forest Algorithm

4 Lessons

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

Decision Trees and Ensembles

Bagging

Feature Randomization

Quiz: Random Forests

11.

Using Random Forests

7 Lessons

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

Random Forests with tidymodels

Feature Importance

Tuning Random Forests

Preparing the Test Dataset

Making Predictions

Testing the Titanic Dataset

Quiz: Using Random Forests

12.

Gradient Boosting Trees

6 Lessons

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

How Gradient Boosting Works

Gradient Boosting with XGBoost

Using XGBoost with tidymodels

Tuning XGBoost with tidymodels

Evaluating XGBoost with tidymodels

Quiz: XGBoost

13.

Continuing Your Journey

1 Lessons

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

What's Next?

Credit Card Fraud Detection using the R Language

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

David Langer

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