Feature Selection — Complete Guide
Feature Selection — 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 27 of 100
Feature Selection
Foundations ✓ → Models → NLP & advanced → MLOps
Models · 2 — Classify & regress · ~6 min · Module 3: ML.NET Pipelines
What is this?
Feature selection keeps informative columns and drops noise that hurts generalization.
Why should you care?
AIPredict fraud has dozens of candidate signals — not all help AUC.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
var pipeline = ml.Transforms.Concatenate("Features", "Amount", "Hour", "MerchantRisk", "DeviceRisk")
.Append(ml.Transforms.FeatureSelection.SelectFromPermutationFeatureImportance(
"Features", "Features", labelColumnName: "Label", numberOfSlotsToRetain: 3));What happened?
- Start with domain picks; use importance to prune.
- Retrain after drops.
- Watch leakage.
Practice next
- Train with all candidates.
- Inspect importance.
- Retain top slots and retrain.
- Remove DeviceRisk and compare AUC.
- Document kept feature list.
Remember
Keep useful signals. Prune noise. Retrain + compare.
AIPredict feature prune
Top-3 slots retained for fraud.
Outcome: Simpler model, similar AUC.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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