Gradient Boosting in R: A Hands-On Interactive Course

Gradient boosting wins more tabular-data competitions than any other method, and it is far simpler than it looks. This six-lesson interactive course builds it from the ground up: how each shallow tree fixes the errors of the trees before it, what the learning rate really controls, and how to tune, constrain and read a real booster in R.

A random forest grows hundreds of trees in parallel and lets them vote. Boosting takes the opposite path: it grows trees one at a time, and each new tree exists for a single job, to correct the mistakes the model is still making. That one idea, repeated, is the engine behind XGBoost, LightGBM and CatBoost.

Most tutorials start at "call this function." This course starts one level deeper, with the residual a single tree leaves behind, and builds up until you can train, tune and trust a booster on your own data and explain exactly why every knob is there.

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

The six lessons

Lesson 1: Gradient Boosting from Scratch

Build boosting by hand. Each shallow tree is trained on what the current model still gets wrong (the residuals), and you watch those errors shrink step by step. See what the learning rate does, and learn precisely how boosting differs from bagging.

Start Lesson 1: Gradient Boosting from Scratch

Lesson 2: LightGBM and CatBoost in R

The modern, fast boosters. Histogram-based splits that train in a fraction of the time, native handling of categorical columns, and a clear rule for when to reach for which.

Start Lesson 2: LightGBM and CatBoost

Lesson 3: The Hyperparameters That Matter

Boosting has many knobs, but only a few move the needle. The number of trees, the learning rate, tree depth and regularization, and how they trade off against one another.

Start Lesson 3: The hyperparameters that matter

Lesson 4: Early Stopping and Learning Curves

Read the training and validation curves together, stop adding trees the moment validation error turns back up, and see the rounds-versus-error picture that tells you when to quit.

Start Lesson 4: Early stopping and learning curves

Lesson 5: Monotonic Constraints for Business Rules

Force a feature to push predictions only one way (higher income should never lower a credit score), see why a business often requires it, and weigh the small accuracy cost you pay for that trust.

Start Lesson 5: Monotonic constraints

Lesson 6: Quantile Regression Forests and Prediction Intervals

Predict a range, not just a single number. Quantile loss, and how to turn a tree ensemble into honest prediction intervals you can put in front of a decision maker.

Start Lesson 6: Prediction intervals

Who this is for

You are comfortable running R and know what a training and test set are. Having met decision trees or random forests helps, but it is not required: Lesson 1 builds the idea from a single tree. You do not need any prior boosting experience.

What you will be able to do

• Explain boosting as sequential error-correction and describe how fitting residuals, scaled by the learning rate, builds an accurate model from weak trees
• Choose between XGBoost, LightGBM and CatBoost and know what histogram splits and native categorical handling buy you
• Tune the handful of hyperparameters that actually matter and use early stopping to stop before you overfit
• Apply monotonic constraints to satisfy a business rule, and produce calibrated prediction intervals instead of single-point guesses

Ready? Begin with Lesson 1.