Imbalanced Classification in R: A Hands-On Course

Accuracy is the first number everyone reports and the first one to lie to you. When one class is rare, or when a miss costs far more than a false alarm, a model can be 99% accurate and completely useless. This six-lesson interactive course teaches imbalanced and multiclass classification in R from the ground up: how to measure a classifier honestly, how to rebalance without cheating, where to put the decision threshold, and how to trust the probabilities a model reports. You run live R in the browser at every step.

A fraud detector that flags nothing is right 99.9% of the time and catches zero fraud. A disease screen that always says "healthy" scores beautifully on accuracy and sends every sick patient home. The problem is not the algorithm; it is that the usual score rewards the wrong thing when the classes are lopsided or the mistakes are not equal.

This course builds the judgment to handle those cases. It starts with what changes when you move past two classes, then works through the rare-positive problem end to end: rebalancing the training data safely, moving the threshold to match real costs, reading the curve families that expose what accuracy hides, and calibrating probabilities so a predicted 0.7 actually happens 70% of the time. The thread through every lesson is the same discipline: judge the model by the decision it drives, not by the number that flatters it.

Each lesson is a guided, interactive experience: you run live R in the browser, answer checkpoints, and write code as you go.

The six lessons

Lesson 1: Beyond Binary: Multiclass Classification

Most of what you know for two classes needs a small twist for three or more. Learn one-vs-rest and one-vs-one decomposition, the K-by-K confusion matrix, and how macro, micro and weighted precision and recall each tell a different story.

Start Lesson 1: Beyond Binary: Multiclass Classification

Lesson 2: Class Imbalance and Resampling

When positives are rare, the model learns to ignore them. Learn under- and oversampling and SMOTE with themis, and the one rule that keeps a resampled score honest: rebalance the training folds only, never the data you evaluate on.

Start Lesson 2: Class Imbalance and Resampling

Lesson 3: Thresholds Under Asymmetric Costs

The default 0.5 cutoff assumes a false negative and a false positive hurt equally. They rarely do. Learn to move the threshold deliberately when missing a positive costs far more than a false alarm, and read the trade-off straight off the confusion matrix.

Start Lesson 3: Thresholds Under Asymmetric Costs

Lesson 4: ROC, PR, Lift and Gains Curves

One threshold is one point; a curve shows every threshold at once. Learn to read ROC, precision-recall, lift and gains curves, and why precision-recall tells the truth on rare positives where ROC looks deceptively good.

Start Lesson 4: ROC, PR, Lift and Gains Curves

Lesson 5: Calibrating Predicted Probabilities

A model can rank cases perfectly and still report probabilities that are badly wrong. Learn reliability diagrams to see the gap, then Platt scaling and isotonic regression to fix it, so a predicted 0.7 means it really happens about 70% of the time.

Start Lesson 5: Calibrating Predicted Probabilities

Lesson 6: Why AUC Is Not Enough

AUC is the metric everyone quotes, and a single AUC number hides a lot. Learn what it cannot see, which second metric to pair it with for your problem, and how two models with the same AUC can behave very differently in production.

Start Lesson 6: Why AUC Is Not Enough

Who this is for

You can fit a classifier in R and read a confusion matrix, and you know what precision and recall mean for two classes. Having wrestled with a rare-class problem, where accuracy looked great but the model missed everything that mattered, helps you feel why this course exists, but each idea is explained as it arrives. You do not need any prior experience with SMOTE, ROC curves or calibration.

What you will be able to do

• Score a multiclass model fairly with per-class results and macro, micro and weighted averages
• Rebalance a rare-positive dataset with resampling and SMOTE without leaking into evaluation
• Move the decision threshold to match the real cost of a false negative versus a false positive
• Read ROC, precision-recall, lift and gains curves, and know which one to trust on rare positives
• Calibrate predicted probabilities so the numbers a model reports can be used as real probabilities

Ready? Begin with Lesson 1.