Regression Basics — Complete Guide
Regression Basics — 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 41 of 100
Regression Basics
Foundations ✓ → Models → NLP & advanced → MLOps
Models · 2 — Classify & regress · ~6 min · Module 5: Regression Models
What is this?
Regression predicts a float Label from Features — sales, price, demand.
Why should you care?
AIPredict forecasting modules start with a simple Sdca or FastTree regressor.
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.CopyColumns("Label", "Sales")
.Append(ml.Transforms.Concatenate("Features", "Lag1", "Month", "Promo"))
.Append(ml.Regression.Trainers.Sdca());
var metrics = ml.Regression.Evaluate(model.Transform(test));
Console.WriteLine($"MAE={metrics.MeanAbsoluteError:F2}");What happened?
- Label must be float.
- MAE/R² guide quality.
- Time-based split for forecasts.
Practice next
- CopyColumns Label.
- Sdca Fit.
- Print MAE.
- Add Lag7.
- Try FastTree regression.
Remember
Float Label. MAE/R². Time-aware split.
AIPredict sales baseline
Sdca predicts next-day sales.
Outcome: MAE beats naive lag-1.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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