Machine Learning Academy · 4 / 5

Start with a bounded baseline

Make a small, reviewable initial configuration before attempting tuning.

YOUR TASK

Make this happen

3 steps

Add baseline hyperparameters: maxDepth 3, eta 0.1, rounds 100 and evalMetric rmse. Connect them to the tabular model.
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 baseline hyperparameters: maxDepth 3, eta 0.1, rounds 100 and evalMetric rmse. Connect them to the tabular model.

Files to work in

model.cococo, tuning.cococo. 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.

Complexity is not accuracy

Deeper trees can overfit. These settings define a starting experiment, not an optimal model. Change one justified aspect at a time and record comparisons.

Interpret the reported score

Streaming tabular training metrics are in-sample diagnostics, not held-out validation. That path has no configurable validation split or early stopping and evaluationHistory is empty. Score separate data and compare with a mean baseline before approval.

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, tuning.cococo

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]
  normalizationStages = [ml_model_normalization_stage.clean]
}

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("scripts/normalize.lua")
}

scripts/normalize.lua

if input.priority == nil then return nil end
return { quantity = input.quantity, priority = input.priority }

tuning.cococo

ml_model_hyperparameters "baseline" {
  # TODO: add maxDepth — see the task above.
  # TODO: add eta — see the task above.
  # TODO: add rounds — see the task above.
  # TODO: add evalMetric — see the task above.
}

Supplied supporting files

Reference: machine-learning · authoring