R tidymodels part 3: Classification

R, Data Science, tidymodels, Machine Learning, Classification, Logistic Regression, KNN, Naive Bayes, Metrics, RStudio

What you'll learn
- What classification is and how it differs from regression
- How to interpret probabilities, odds, and log-odds in logistic regression
- How logistic regression models are estimated using maximum likelihood
- How to fit and interpret logistic regression models in tidymodels
- What are the key classification metrics and how to calculate them
- How to use accuracy, precision, recall, specificity, and F1 score
- How to visualize and interpret ROC curves
- How probability thresholds affect classification results
- What happens when data are imbalanced and how to handle it
- How to use balanced accuracy and advanced metrics for imbalanced data
- How to tune probability thresholds and evaluate precision–recall curves
- What resampling techniques exist for imbalanced data (up-sampling, down-sampling, SMOTE)
- How to apply resampling techniques inside the tidymodels framework
- How to train and tune KNN classifiers in tidymodels
- What Bayes’ theorem is and how it leads to the Naive Bayes classifier
- How the Naive Bayes classifier works for discrete and continuous features
- How to build and compare Naive Bayes models in tidymodels

Requirements
- Finishing “R tidymodels part 2: Beyond linear regression” is strongly recommended.
- Familiarity with the tidymodels framework and ML workflows covered in previous parts.
- R and RStudio already installed on your computer.
- Basic knowledge of statistics (concepts such as probability, regression and classification) is a plus.
- Intermediate R knowledge and experience with tidyverse syntax are recommended.
- If you are a complete beginner to programming or R, you may find this course challenging.
- Interest in data science, machine learning, and classification modeling.
- Curiosity about understanding probabilities, metrics, and model evaluation.
- Interest in writing clean, efficient, and reproducible R code.
- Please update R and its libraries if necessary. A list of versions (R and all R libraries used in the exercises) is provided at the end of each section.

Description
You’vemastered regression modelingandexplored advanced algorithms; now it’s time to step into the world ofclassification.

This course is designed for learners who want to build models thatpredict categories, not numbers, and who wish to understand the statistical and machine learning foundations behind them.

What You’ll Learn

In this course, you’ll move from probability intuition to full-scale classification workflows usingtidymodels, R’s modern ecosystem for machine learning:

Understand what classification is and how it differs from regression

Learn the logic oflogistic regressionand its link to probabilities, odds, and log-odds

Fit and interpret logistic regression models usingmaximum likelihood estimation

Evaluate models withaccuracy, precision, recall, specificity, and F1 score

Visualize model performance throughROC and AUC curves

Adjustprobability thresholdsand see their effect on predictions

Handleimbalanced datausing balanced accuracy, threshold tuning, and advanced metrics

Apply resampling techniques such asupsampling, downsampling,andSMOTE

Build and tuneK-nearest neighbors (KNN)classifiers

ExploreNaive Bayesas a probabilistic classifier for both numeric and text data

Preprocess text usingtextrecipesand create a simple spam-filtering model

Compare multiple classification models within the same tidymodels workflow

Why Take This Course?

Classification problems are everywhere — from medical diagnostics and fraud detection to email filtering and customer segmentation.This course helps you understand how these models make decisions andhow to evaluate them responsibly.

You’ll gain not only the technical skills to build classification models but also the intuition to select the right metric and interpret model behavior — all while keeping your worktidy, reproducible, and explainable.

What You’ll Get

Clear, structured explanations ofclassification theoryandpractice

Step-by-step modeling workflows inR and tidymodels

Real-world examples and visual explanations of metrics

Exercisesandassignmentswith full solutions

All code, datasets, and outputs provided

Lifetime access and updates

Who this course is for:
- Anyone interested in data science or machine learning
- Learners who want to understand classification modeling in R
- Anyone who wants to analyze and predict categorical outcomes
- Data analysts and scientists who want to extend their tidymodels skills
- Anyone curious about logistic regression, KNN, or Naive Bayes algorithms
- Those who wish to learn how to evaluate models using real-world metrics
- Professionals who work with imbalanced data or probability-based predictions
- Students and researchers building classification models
- R users aiming to deepen their modeling expertise within the tidyverse/tidymodels ecosystem
- Data scientists who mainly use Python and want to extend their skills into R
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