Hold Out Test
Use holdout tests in Alviss AI to validate model performance on unseen data by reserving recent periods for evaluation.
Holdout tests in Alviss AI are a key validation feature integrated into the model building interface, allowing you to reserve a portion of your dataset for evaluating model performance on unseen data. By splitting your data into training and holdout periods, you can assess how well your models generalize, detect overfitting, and ensure reliable insights for attributions, simulations, and predictions.
Alviss AI supports multiple holdout sets for more robust validation, with visual representations of holdout areas to compare predicted versus actual outcomes.
Holdout periods should be selected based on your data's time series nature—typically, use recent data as holdout to simulate future performance. Ensure your dataset has sufficient observations to avoid underfitting.
Benefits of Holdout Tests
- Improved Model Reliability: Validate against unseen data to ensure your models perform well in real-world scenarios, reducing the risk of biased insights.
- Overfitting Prevention: Identify models that memorize training data but fail on holdouts, guiding refinements in the Advanced Builder.
- Data-Driven Confidence: Quantify model accuracy before deploying to Attributions, Simulations, or Predictions, supporting better business decisions.
- Efficiency in Collaboration: Shared holdout configurations within teams ensure consistent validation across projects.
By incorporating holdout tests, you enhance the trustworthiness of your AI models, aligning with Alviss AI's focus on scalable, accurate, and actionable decision-making. For advanced validation techniques, consider combining with cross-validation or exploring custom nodes in the graph editor.