ML Workflow & Evaluation
Frame a problem, split data correctly, choose metrics, detect overfitting, and compare against a baseline.
Concept overview
What this lesson will help you understand.
A trustworthy workflow turns a useful question into reproducible evidence and protects evaluation data from accidental reuse.
Learn by doing
Explore the concept.
Make a prediction before changing a control. Run the experiment, explain what moved, and compare the result with the theory below.
Initializing Interactive Playground...
Complete lesson
Detailed concept deep dive.
Build intuition first, then work through implementation, mathematical derivations, failure analysis, and real system decisions.
7 guided chapters
A trustworthy workflow turns a useful question into reproducible evidence and protects evaluation data from accidental reuse.
Before working through the formal derivations, connect the vocabulary to one small, concrete example. The goal is not to memorize definitions in isolation. It is to understand what each idea represents, which assumptions make it valid, and how the pieces relate to one another.
Ideas to understand
- Problem framing, target, unit, prediction time, and baseline
- Train, validation, test, cross-validation, and temporal splits
- Regression and classification metrics, confusion matrix, calibration, and thresholds
Learn by doing
Make the idea concrete
Explain the ML workflow and evaluation with a concrete example and no unexplained jargon.
Try this
- Define “Problem framing, target, unit, prediction time, and baseline” in your own words, then annotate one concrete ML Workflow & Evaluation input and output.
- Construct one valid case and one counterexample for “Train, validation, test, cross-validation, and temporal splits”; explain which assumption separates them.
- Predict how “Regression and classification metrics, confusion matrix, calibration, and thresholds” will change one visible playground result, then test and record the before/after values.
Evidence of understanding
- You can explain the example without relying on jargon.
- You can name the assumptions and identify what would invalidate them.
- You can connect the example to at least one real ML use case.
What part of the ML Workflow & Evaluation mental model still feels least intuitive, and what example would help clarify it?
Saved in your browser progress and included in exports.
Practice with code
Make the idea executable.
Complete the starter code, use progressive hints, and pass deterministic checks in the browser.
Runnable Python lab
Compute a confusion matrix and threshold tradeoff
Implement confusion counts for fixed probabilities and compare a default threshold with a recall-oriented threshold.
Keep learning
Papers, standards, and practical references.
Start with the free primary sources. Books are included where a longer, connected treatment is worth the investment.
scikit-learn User Guide
Free reference for preprocessing, model selection, metrics, and classical ML algorithms.
Rules of Machine Learning
Free, practical guidance on baselines, pipelines, metrics, and production ML iteration.
Model Cards for Model Reporting
Research paper proposing transparent documentation of model performance and limitations.
Test your understanding.
Answer explanations appear after every choice. Missed questions can be reviewed before a full retake.
Mastery requires 80% or higher.
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