Model Evaluation and Tuning in R: A Hands-On Course
A model is only as trustworthy as the number you use to judge it. This seven-lesson interactive course teaches model evaluation and tuning in R from the ground up: how to estimate real accuracy instead of trusting one lucky split, how to tune a model without fooling yourself, and how to tell whether one model is genuinely better than another. You run live R in the browser at every step.
The score you report is a decision in disguise. Ship a model on the strength of a single train/test split and you are betting on the luck of the draw; tune it against the same data you evaluate on and the number flatters you right up until production. Most of the damage in applied machine learning is not a bad algorithm, it is a good algorithm measured badly.
This course fixes that. It builds the judgment to answer three questions honestly: what accuracy will this model really have, which settings should it use, and is it actually better than the alternative? The thread running through every lesson is the same discipline, made concrete again and again: the data you use to decide must never be the data you use to report.
Each lesson is a guided, interactive experience: you run live R in the browser, answer checkpoints, and write code as you go.
The seven lessons
Lesson 1: Cross-Validation Strategies
One train/test split gives a score that is part skill, part luck. Learn k-fold, repeated k-fold and LOOCV, why they give a steadier estimate than a single split, and the bias-variance trade-off in the estimate itself.
Start Lesson 1: Cross-Validation Strategies
Lesson 2: Grouped, Blocked, and Time-Aware CV
Plain random folds quietly leak when rows are grouped or ordered in time, and the leaked score looks great. Learn grouped, blocked and time-aware resampling that keeps related rows together and never trains on the future.
Start Lesson 2: Grouped, Blocked, and Time-Aware CV
Lesson 3: Nested Cross-Validation
If you tune and evaluate on the same folds, the reported score is optimistic by design. Learn nested cross-validation: tune on the inside loop, evaluate on the outside, so the number you publish is one the model has genuinely earned.
Start Lesson 3: Nested Cross-Validation
Lesson 4: Hyperparameter Tuning Strategies
The right settings can matter as much as the right model, but a search can burn a huge budget for tiny gains. Learn grid, random and Bayesian search, and how to spend a limited tuning budget where it actually pays off.
Start Lesson 4: Hyperparameter Tuning Strategies
Lesson 5: Scoring Rules and Regression Metrics
The metric you optimize is the behavior you get. Learn proper scoring rules and log-loss for probabilities, RMSE, MAE and MAPE for regression, and exactly what each one rewards and punishes so you pick the metric that matches the decision.
Start Lesson 5: Scoring Rules and Regression Metrics
Lesson 6: Comparing Models Statistically
Model A beat model B on one split. Does that mean anything? Learn to compare models on resampled differences with a measure of uncertainty, so you separate a real improvement from single-split luck.
Start Lesson 6: Comparing Models Statistically
Lesson 7: From Metrics to Money
A metric gain is only worth the decision it changes. Learn to translate an accuracy or AUC improvement into the business outcome it should drive, so the model you choose is the one that pays.
Start Lesson 7: From Metrics to Money
Who this is for
You can run R and read its output, and you have fit at least one model and know what a train/test split is. Having tuned a model or wrestled with a score that would not reproduce helps you feel why this matters, but each idea is explained as it arrives. You do not need any prior experience with cross-validation or tuning.
What you will be able to do
- Estimate a model's real accuracy with k-fold, repeated and nested cross-validation instead of one lucky split
- Resample safely when rows are grouped or ordered in time, so nothing leaks from test to train
- Tune hyperparameters with grid, random and Bayesian search, and spend a limited budget well
- Choose scoring rules and regression metrics that match the decision, and read what each one rewards
- Compare models with uncertainty, and turn a metric gain into the business decision it should drive
Ready? Begin with Lesson 1.