Evaluation Metrics — Complete Guide
Evaluation Metrics — 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 19 of 100
Evaluation Metrics
Foundations → Models → NLP & advanced → MLOps
Foundations · 1 — Context & data · ~6 min · Module 2: Machine Learning Basics
What is this?
Metrics quantify model quality — AUC/F1 for classification, MAE/R² for regression, NDCG for ranking.
Why should you care?
AIPredict go-live needs numbers, not “looks good in a demo”.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
var metrics = ml.BinaryClassification.Evaluate(predictions);
Console.WriteLine($"AUC={metrics.AreaUnderRocCurve:F3} F1={metrics.F1Score:F3}");What happened?
- Pick metrics that match the business cost of errors.
- Log them with model version.
Practice next
- Evaluate binary AUC/F1.
- Evaluate regression MAE.
- Store metrics JSON next to zip.
- Print confusion matrix.
- Compare two trainers on same split.
Remember
Right metric family. Business cost. Version + metrics.
AIPredict metric gate
CI fails if AUC < 0.85.
Outcome: Bad models never deploy.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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