Missing Value Handling — Complete Guide
Missing Value Handling — 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 26 of 100
Missing Value Handling
Foundations ✓ → Models → NLP & advanced → MLOps
Models · 2 — Classify & regress · ~6 min · Module 3: ML.NET Pipelines
What is this?
Replace or drop missing values before trainers that require dense Features.
Why should you care?
AIPredict CSVs often have null Hour or MerchantCategory from upstream gaps.
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.ReplaceMissingValues("Amount", "Amount",
MissingValueReplacingEstimator.ReplacementMode.Mean)
.Append(ml.Transforms.ReplaceMissingValues("Hour", "Hour",
MissingValueReplacingEstimator.ReplacementMode.DefaultValue));What happened?
- Mean/mode for numerics; default or unknown bucket for categories.
- Log how often you impute.
Practice next
- ReplaceMissingValues on Amount.
- DefaultValue on Hour.
- Compare row counts before/after drop strategy.
- Try Mode for a category code.
- Fail job if null Label > 0.
Remember
Impute or drop. Log rates. Fit learns replace maps.
AIPredict null Hour fix
Missing Hour filled with default.
Outcome: Trainer stops throwing on NaN.
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!
