Binary Classification — Complete Guide
Binary Classification — 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 31 of 100
Binary Classification
Foundations ✓ → Models → NLP & advanced → MLOps
Models · 2 — Classify & regress · ~6 min · Module 4: Classification Models
What is this?
Binary classification predicts one of two labels — fraud/legit, churn/stay — with a probability.
Why should you care?
AIPredict fraud and churn modules are binary at the core.
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")
.Append(ml.BinaryClassification.Trainers.FastTree());
var model = pipeline.Fit(train);
var m = ml.BinaryClassification.Evaluate(model.Transform(test));
Console.WriteLine($"AUC={m.AreaUnderRocCurve:F3}");What happened?
- Label is bool.
- Threshold converts Probability to action.
- Optimize for cost of false positives vs misses.
Practice next
- FastTree binary.
- Evaluate AUC.
- Try threshold 0.7 for alerts.
- Print F1 at 0.5 and 0.8.
- Swap to SdcaLogisticRegression.
Remember
Two classes. AUC + threshold. Cost-aware cutoffs.
AIPredict fraud binary
FastTree scores IsFraud.
Outcome: AUC baseline for fraud.zip.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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