Classification Optimization — Complete Guide
Classification Optimization — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.
On this page
ML.NET Tutorial · Lesson 40 of 100
Classification Optimization
Foundations ✓ → Models → NLP & advanced → MLOps
Models · 2 — Classify & regress · ~6 min · Module 4: Classification Models
What is this?
Optimize classifiers with better features, calibrated thresholds, class weights, and trainer choice.
Why should you care?
AIPredict lifts fraud catch rate without exploding false positives.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
var options = new FastTreeBinaryTrainer.Options {
NumberOfTrees = 200, MinimumExampleCountPerLeaf = 10
};
// also: tune threshold on validation for precision@recall targetWhat happened?
- Fix data/features first.
- Then hyperparameters.
- Then threshold for business KPIs.
Practice next
- Tune trees.
- Sweep threshold for precision.
- Compare FastTree vs LightGbm.
- Set early stopping.
- Plot precision-recall curve.
Remember
Features → hyperparams → threshold. Holdout sacred. Track FP cost.
AIPredict fraud tune
Threshold set for precision 0.9.
Outcome: Fewer angry false blocks.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
Sign in to ask a question or upvote helpful answers.
No questions yet — be the first to ask!
