Practical Machine Learning (Machine Learning)

Start with the problem

A machine-learning project should begin with a precise task and success criterion, not with a model. The team should define what input is available at prediction time, what the target represents, what decisions depend on the output and what errors matter.

Baselines

A simple baseline provides a reference point. Depending on the task this may be a constant predictor, linear model, heuristic, retrieval system or existing business rule.

Without a baseline, it is easy to mistake model complexity for progress.

Data quality

Missing values
Incorrect labels
Duplicate records
Sampling bias
Temporal leakage
Train-test contamination
Unrepresentative deployment data

In many projects, improving the data-generating and labelling process produces more reliable gains than swapping one sophisticated model for another.

Validation strategy

The validation scheme should match deployment. Random splits can be inappropriate for temporal prediction, grouped observations, repeated measurements or data with strong correlations.

A test set should remain sufficiently isolated from iterative model development to preserve its role as an unbiased estimate of final performance.

Error analysis

Aggregate metrics hide structure. A useful error-analysis process identifies representative failures, groups them by cause, estimates their frequency and determines which intervention is likely to reduce them.

Improve data
Change labels
Change features
Change model
Change threshold
Add a specialized component
Redefine the task

Deployment and monitoring

A model is part of a system. Production reliability depends on input validation, latency, resource usage, model versioning, observability, rollback procedures and monitoring for data and performance changes.