method#Machine Learning#Analytics#Data

Cross-Validation

Statistical technique for robustly evaluating and comparing predictive models by repeatedly splitting data into training and test sets.

Cross-validation is a statistical technique for evaluating predictive models by repeatedly partitioning datasets into training and test folds; it reduces overfitting and provides more reliable performance estimates.

Maturity

Established

Cognitive loadMedium

360° overview

Cross-Validation

Six perspectives place the building block in context. The numbers show where each perspective continues in the reading path.

The building block at a glance

Cross-Validation

360°

Classification

2

Complexity: Medium
Impact area: Technical

Technical context

2

Integrations

scikit-learn (model_selection)

+2

Principles & goals

1

Use a validation strategy matching the data structure

Value stream stage: Build

Use cases & scenarios

4

Use cases: 3
Scenarios: 3

Compromises

3

Risks: 3
Best practices: 3

I/O & resources

4

Inputs: 3
Outputs: 3