Machine Learning Model Evaluation and Validation
Model Evaluation and Validation is an intermediate-level course of 5 lessons (about 2 h 55 min), with 12 exercises and 15 quiz questions. Prerequisites: Supervised Learning: Regression and Classification. Exercises run in the browser, with nothing to install.
- Data science
- Intermediate
- 5 lessons
- 2 h 55 min
- 12 exercises
- Exercises run in the browser
The course in brief
This course teaches you to measure the real performance of a machine learning model and to avoid evaluation pitfalls. You choose metrics suited to the cost of errors, tune a decision threshold, build cross-validation strategies that mirror production, optimize hyperparameters with scikit-learn, and diagnose bias, variance, and data leakage.
What you will be able to do
- Choose and compute classification metrics suited to the context (precision, recall, F1, AUC, AP)
- Tune a decision threshold without using the test set
- Build a cross-validation scheme that respects groups and time
- Optimize hyperparameters with GridSearchCV and RandomizedSearchCV and report an honest performance
- Diagnose bias and variance with validation curves and learning curves
- Detect and fix data leakage
Syllabus: 5 lessons
- Read a confusion matrix
- Compute precision, recall, and F1 and know which to favor
- Recognize the accuracy trap on imbalanced classes
- 02
ROC and Precision-Recall Curves, Choosing the Threshold
- Evaluate a classifier independently of the threshold with ROC AUC and average precision
- Read a ROC curve and a precision-recall curve
- Choose a decision threshold without using the test set
- 03
Cross-Validation Strategies
- Choose a suitable split: KFold, StratifiedKFold, GroupKFold, TimeSeriesSplit
- Evaluate several metrics at once with cross_validate
- Interpret the mean, the standard deviation, and the train/validation gap
Cross-validationGroup and time-based validationcross_validate
- 04
Hyperparameter Search: GridSearchCV and RandomizedSearchCV
- Tune the hyperparameters of a pipeline with GridSearchCV
- Explore a large space with RandomizedSearchCV and distributions
- Honestly evaluate a tuned model (final test or nested validation)
- 05
Diagnosing a Model: Curves and Data Leakage
- Read a validation curve and a learning curve
- Tell high bias from high variance and choose the right remedy
- Spot and fix the main data leaks
Bias-variance trade-offValidation and learning curvesData leakage
Final project
Evaluation Audit of a Fraud Detection Model
Use make_classification to generate an imbalanced transaction dataset (about 3% fraud) with a customer identifier (several transactions per customer). Build a complete evaluation process: baseline, a reasoned choice of the primary metric, cross-validation that respects customers, hyperparameter search for at least two model families, threshold tuning based on a business cost (for example 5 € per unnecessary check, 300 € per missed fraud), diagnosis with curves, and verification that there is no leakage. Report the final performance on a test set isolated by customers.
Deliverables
- A reproducible notebook or script covering the entire process
- The ROC and precision-recall curves of the chosen model and a learning curve
- A comparison table of the tuned models (cross-validation mean and standard deviation)
- A note justifying the metric, the split, the chosen threshold, and the expected cost
Assessment criteria
- Primary metric consistent with the business cost and comparison to a baseline
- Split that respects groups and a test set used only once
- Correct hyperparameter search (pipeline, step__parameter names, unbiased performance)
- Threshold chosen outside the test set and justified by cost
- Bias/variance diagnosis and demonstrated absence of leakage
Frequently asked questions
Why isn't accuracy enough to evaluate a model?
On imbalanced classes, a model that always predicts the majority class gets a high accuracy without detecting anything. Precision, recall, F1, balanced accuracy, and precision-recall curves describe each type of error.
What is the difference between a validation set and a test set?
The validation set, or cross-validation on the training data, is used to choose the model, the hyperparameters, and the threshold. The test set stays untouched until the end and is used only once, to give an honest estimate of the performance of the chosen model.
What is data leakage?
It is the use, during training or evaluation, of information that is not available in production: preprocessing fit on all the data, a variable recorded after the event, duplicates between train and test. It produces scores that are too good to be true.
GridSearchCV or RandomizedSearchCV?
GridSearchCV tests all combinations of a small grid. RandomizedSearchCV samples a fixed number of combinations from distributions: it is more efficient when the space is large or continuous.
This course, told for your job
Say what you want to be able to do: “Model Evaluation and Validation” takes its place in your path, with examples from your field. The concepts stay those of the course.