Teacher since April 2026
Lukas Brenner
Model evaluation you can trust: splits, metrics, leakage and tuning
9
tutors built
4.5Sample
average from 22 reviews
439Sample
lessons taught by their tutors
About Lukas
Most of the machine learning failures I have seen were not about the algorithm. They came from a test set that was used too often, a feature that leaked the answer, or a metric that hid the real cost of mistakes. I teach the unglamorous discipline that makes model results believable: how to split data, how to validate, which metric matches the decision, and how to tune without fooling yourself. My work background is in building and reviewing predictive models for operations teams, and I teach with real failure stories and small, checkable exercises.
Knows about
Tutors by Lukas
9 tutors
Imbalanced Classes, Handled Carefully
Model rare events like fraud or failures without tricks that quietly backfire86 lessonsSampleLukas Brenner$7Classification Metrics Beyond AccuracyClassification Metrics Beyond Accuracy
Read a confusion matrix and pick the metric that matches the cost of each mistake83 lessonsSampleLukas Brenner$4Regression Metrics and Residual AnalysisRegression Metrics and Residual Analysis
Choose between MAE, RMSE and friends, then read residuals to find what your model misses73 lessonsSampleLukas Brenner$4End to End Tabular ML ProjectEnd to End Tabular ML Project
Take one tabular dataset from question to tested model to clear write up72 lessonsSampleLukas Brenner$9Train, Validation and Test SplitsTrain, Validation and Test Splits
Split your data so your model's score means something outside your laptop48 lessonsSampleLukas BrennerFreeData Leakage DetectiveData Leakage Detective
Find the hidden leaks that make models look brilliant in testing and fail in production43 lessonsSampleLukas Brenner$10Cross Validation Without Fooling YourselfCross Validation Without Fooling Yourself
Use k fold, grouped, time series and nested cross validation correctly34 lessonsSampleLukas Brenner$6Hyperparameter Tuning on a BudgetHyperparameter Tuning on a Budget
Search smarter, spend less compute and avoid overfitting your validation setLukas Brenner$11Overfitting and RegularisationOverfitting and Regularisation
Diagnose overfitting with learning curves and fix it with the right kind of regularisationLukas Brenner$6Recent reviews
What students said about Lukas's tutors.
- Samuel T.Sample
Clear and practical. The precision recall versus ROC lesson was the most useful. Calibration part was a bit short.
- Oskar L.Sample
Clear checklists. Would have liked more on cost weighted metrics, but overall solid.
- Kemal Y.Sample
The grouped split lesson explained why my model looked amazing in testing and poor in production: the same customers were in both sets.
- Hannah W.Sample
Free, short and exactly what our analysts needed. The 'sealed exam paper' rule is now on our team wiki.
- Beatriz C.Sample
I review vendor models at work and now I always ask for the base rate and the threshold. The spam example with the always-negative baseline was eye opening.
- Rohan P.Sample
Clear explanations. The time series lesson was brief but it pointed me in the right direction.