ML Workflow & Evaluation

Frame a problem, split data correctly, choose metrics, detect overfitting, and compare against a baseline.

Not started3 min explanation

Visualize, practice, and deep-dive material are optional—use only what helps you learn.

Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

Frame a measurable ML task
Split and evaluate data correctly
Detect overfitting and choose decision-aware metrics

Helpful before starting

Basic probability and averages
Python and data-table fundamentals
Awareness of features and labels

Start here

ML Workflow & Evaluation, in plain language

Frame a problem, split data correctly, choose metrics, detect overfitting, and compare against a baseline. A trustworthy workflow turns a useful question into reproducible evidence and protects evaluation data from accidental reuse.

For a small example, predict whether six students pass when five already pass. Compare the model with the majority baseline; accuracy alone exposes that a constant predictor already scores highly. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

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.

Use it well

When ML Workflow & Evaluation helps—and where it breaks

Fraud is rare and false blocks are costly. Choose time-aware splits, report precision-recall and cost at a threshold, then inspect errors by customer segment. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

Tuning on the test set. Better approach: Use validation for choices and test once for the final estimate.
Reporting accuracy on an imbalanced task. Better approach: Use confusion-based, ranking, and calibration metrics suited to the decision.
Ignoring repeated entities. Better approach: Use grouped or temporal splits that match deployment.

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