Classification — Complete Guide
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 11 of 100
Classification
Foundations → Models → NLP & advanced → MLOps
Foundations · 1 — Context & data · ~6 min · Module 2: Machine Learning Basics
What is this?
Classification predicts discrete labels — spam/ham, fraud/legit, churn/stay.
Why should you care?
Most AIPredict v1 products are classifiers with a probability threshold.
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", nameof(TxRow.Amount), nameof(TxRow.Hour))
.Append(ml.BinaryClassification.Trainers.SdcaLogisticRegression());
var model = pipeline.Fit(train);What happened?
- Binary vs multi-class use different trainers and metrics.
- Thresholds turn Probability into a business decision.
Practice next
- Train SdcaLogisticRegression.
- Evaluate AUC.
- Pick threshold 0.5 then 0.8.
- Swap trainer to FastTree.
- Plot false positive at two thresholds.
Remember
Discrete labels. Probability + threshold. Right metrics family.
AIPredict first classifier
Fraud binary model trains on Amount+Hour.
Outcome: Baseline AUC to beat later.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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