Forum Discussion
Kudo KingpinHow Do You Validate ML Models for Real-World Performance?
# Preventing Data Leakage in Microsoft Fabric: How Do You Validate ML Models for Real-World Performance?
Data leakage is one of the biggest challenges in building reliable machine learning models. A model may achieve excellent validation accuracy but perform poorly in production because of incorrect data splitting, future information leakage, or inconsistent feature engineering.
In Microsoft Fabric, we can use Lakehouse, notebooks, pipelines, and MLflow to build end-to-end machine learning workflows. However, ensuring reliable model validation across different stages can be challenging.
I'd like to understand how the community handles the following:
1. **Data Splitting:** How do you choose between random, stratified, and time-based splitting for different ML problems?
2. **Data Leakage Detection:** What techniques do you use to identify target leakage and data leakage during feature engineering?
3. **Cross-Validation:** How do you implement reliable cross-validation in Fabric notebooks, especially for large datasets?
4. **Model Validation:** How do you ensure that validation results represent real-world production performance?
5. **Experiment Tracking:** How do you use MLflow to maintain reproducibility across different experiments and validation runs?
**My key question:** What best practices would you recommend for designing a leakage-resistant ML validation pipeline in Microsoft Fabric, particularly for time-dependent datasets and imbalanced classification problems?
I'd appreciate practical examples and real-world experiences from the community.
2 Replies
v-aatheequeCommunity Support
Data leakage is one of the common reasons why ML models perform well during validation but fail to deliver similar results in production.
Selecting the appropriate validation strategy is essential. For time-dependent datasets, time-based splitting generally provides a more realistic evaluation than random splitting. For imbalanced classification, stratified sampling helps maintain class distribution across training and validation datasets.
Additionally, ensure that the entire feature engineering and preprocessing pipeline is validated to prevent future information from leaking into training data.
In Microsoft Fabric, MLflow can help track experiment parameters, evaluation metrics and model versions, making validation runs more reproducible and easier to compare.
For more details, refer to:
It would also be helpful to understand how others are implementing these validation strategies in their Microsoft Fabric environments.
- binitafulpagare
Hi v-aatheeque,
Thank you for sharing these valuable insights and the documentation references.
I agree that time-based splitting is essential for time-dependent datasets, while stratified sampling helps maintain class distribution in imbalanced classification problems.
I have one follow-up question:
For time-dependent machine learning problems, how would you handle situations where the data distribution changes over time?
Would you recommend using rolling-window cross-validation, expanding-window validation, or walk-forward validation to evaluate model stability before production deployment?
Also, how would you integrate this validation process with MLflow experiment tracking in Microsoft Fabric?
I'd appreciate your recommendations and practical implementation insights.
Thanks again!
