Feature Engineering — Complete Guide
Feature Engineering — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.
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ML.NET Tutorial · Lesson 9 of 100
Feature Engineering
Foundations → Models → NLP & advanced → MLOps
Foundations · 1 — Context & data · ~6 min · Module 1: ML.NET Foundations
What is this?
Feature engineering creates predictive signals — ratios, time buckets, risk scores — before training.
Why should you care?
AIPredict fraud gains more from HourOfDay and MerchantRisk than from bigger models alone.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
// precompute before LoadFromEnumerable
row.AmountLog = MathF.Log(row.Amount + 1);
row.IsNight = row.Hour is >= 0 and < 6;What happened?
- Do heavy domain features in C# or SQL; use ML.NET for encoding/normalization.
- Document feature meaning.
Practice next
- Add AmountLog.
- Add IsNight bool→float.
- Retrain and compare AUC.
- Add velocity: tx count last hour.
- Remove a weak feature and compare.
Remember
Domain features first. Encode in ML.NET. Watch leakage.
AIPredict night-amount features
Fraud model uses AmountLog + IsNight.
Outcome: AUC rises without changing trainer.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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