Machine Learning Academy · 3 / 5

Handle missing features deliberately

Drop rows without priority rather than inventing an arbitrary numeric value.

YOUR TASK

Make this happen

3 steps

Add the require-priority normalization stage loading scripts/normalize.lua. Drop missing priority rows; return quantity and priority for accepted rows.
Keep the working capabilities from the previous lesson and connect the new pieces to the same solution.
Check the complete solution, then inspect any unmet requirements.

THE IDEA BEHIND IT

What this step adds

Add the require-priority normalization stage loading scripts/normalize.lua. Drop missing priority rows; return quantity and priority for accepted rows.

Files to work in

model.cococo, normalize.cococo, scripts/normalize.lua. Other files are the working baseline. New files include named resource skeletons and TODO guidance. Complete their fields and connect them using the objectives, schema completion and reference guide.

One record at a time

Normalization receives one record as input. Returning nil drops that record; returning a table emits it. There is no global rows collection.

Keep missing values meaningful

SQL NULL and empty CSV cells become missing values. Do not silently substitute zero unless the domain justifies it. Review emitted rows and null counts with the platform preview before training.

Checks validate native model configuration and lesson requirements. They do not run SQL, normalize rows, train a model, score predictions or establish predictive quality.

Files to change: model.cococo, normalize.cococo, scripts/normalize.lua

What you’re building: Author a reviewable job-quantity regression model with explicit features, missing-value handling and deliberate publication.

This step starts with the earlier solution and one new challenge. Each lesson keeps its own draft.

Read the starter files

main.cococo

ml_model_document "main" {
  format = "1"
  name = "job-quantity"
  backend = "xgboost"
  spec = {
    tabular = ml_model_tabular.quantity
  }
  dataQuerySQL = file("queries/training.sql")
}

model.cococo

ml_model_tabular "quantity" {
  task = "REGRESSION"
  targetColumn = "quantity"
  featureColumns = [ml_model_feature.priority]
}

features.cococo

ml_model_feature "priority" {
  column = "priority"
}

queries/training.sql

SELECT quantity, priority FROM jobs WHERE quantity IS NOT NULL LIMIT 1000

normalize.cococo

ml_model_normalization_stage "clean" {
  name = "require-priority"
  # Source file supplied for this field: scripts/normalize.lua
  # TODO: attach it with source = file(...).
}

scripts/normalize.lua

-- input is one training row.
-- TODO: reject missing priority; return only the target and feature fields.
return input

Supplied supporting files

Reference: machine-learning · authoring