Data Testing
Creating Test Cases for Model Input Validation
This prompt helps data science teams design test cases for validating datasets used as inputs for machine learning models. It focuses on ensuring data quality, structure, and alignment with model requirements.
Responsible:
Data Science
Accountable, Informed or Consulted:
Data Science, Engineering, QA
THE PREP
Creating effective prompts involves tailoring them with detailed, relevant information and uploading documents that provide the best context. Prompts act as a framework to guide the response, but specificity and customization ensure the most accurate and helpful results. Use these prep tips to get the most out of this prompt:
Define the machine learning model’s input schema and preprocessing requirements.
Gather training data statistics and characteristics for comparison during testing.
Review the dataset for missing fields, outliers, or incorrect encodings.
THE PROMPT
Help create detailed test cases to validate datasets for use as inputs to [specific machine learning model, e.g., a regression model predicting housing prices]. Focus on:
Schema Validation: Recommending checks, such as, ‘Ensure the dataset matches the expected schema, including column names, data types, and non-null constraints.’
Feature Scaling Checks: Suggesting preprocessing validation, like, ‘Verify that numerical features are scaled appropriately for the model, ensuring no out-of-range values exist.’
Categorical Encoding Validation: Including encoding tests, such as, ‘Ensure that all categorical variables are encoded correctly (e.g., one-hot or label encoding) and match the model’s training data format.’
Distribution Testing: Proposing statistical validations, such as, ‘Compare feature distributions to training data using KS tests or quantile analysis to detect shifts or drifts.’
Missing Value Handling: Recommending checks, such as, ‘Test that imputation or missing value handling rules are applied consistently across all features.’
Provide actionable test cases to validate the dataset’s readiness for model input and alignment with the training dataset’s requirements. If additional details about the dataset or model are needed, ask clarifying questions to refine the test cases.
Bonus Add-On Prompts
Propose strategies for automating input validation checks in ETL pipelines.
Suggest methods for identifying and addressing data drift in production datasets.
Highlight techniques for validating consistency between training and inference datasets.
Use AI responsibly by verifying its outputs, as it may occasionally generate inaccurate or incomplete information. Treat AI as a tool to support your decision-making, ensuring human oversight and professional judgment for critical or sensitive use cases.
SUGGESTIONS TO IMPROVE
Focus on model input validation for specific use cases, like image or text data.
Include tips for testing datasets in real-time or batch inference workflows.
Propose ways to validate datasets against data contracts or feature store definitions.
Highlight tools like TensorFlow Data Validation or Great Expectations for input validation.
Add suggestions for creating automated validation scripts to check for schema mismatches.
WHEN TO USE
During the data preprocessing phase for machine learning pipelines.
To validate production datasets for consistency with model training data.
When debugging issues in model predictions caused by input data discrepancies.
WHEN NOT TO USE
For datasets not intended for machine learning applications.
If the input dataset lacks sufficient structure for validation checks.