Who should use the Explain model predictions workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for explain model predictions with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A refined model with improved fairness or robustness, validated by new explanations.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A refined model with improved fairness or robustness, validated by new explanations.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use FiftyOne to a specific model and prediction instance are locked in, ready for explanation. Then, you pass the output to Captum to a ranked list of global feature importances with visual summary, showing which features matter most overall. Then, you pass the output to Captum to a local explanation (e.g., force plot or lime table) that attributes the prediction to specific feature values. Then, you pass the output to Captum to confidence that the explanation is accurate and not an artifact of the interpretability method. Then, you pass the output to Mistral AI Models to a stakeholder-ready report that explains the prediction clearly without requiring ml expertise. Finally, Tecton is used to a refined model with improved fairness or robustness, validated by new explanations.
Select a model and prediction to explain
A specific model and prediction instance are locked in, ready for explanation.
Compute global feature importance
A ranked list of global feature importances with visual summary, showing which features matter most overall.
Generate local explanation for the selected prediction
A local explanation (e.g., force plot or LIME table) that attributes the prediction to specific feature values.
Validate explanation fidelity
Confidence that the explanation is accurate and not an artifact of the interpretability method.
Communicate findings to stakeholders
A stakeholder-ready report that explains the prediction clearly without requiring ML expertise.
Iterate on model improvement (optional)
A refined model with improved fairness or robustness, validated by new explanations.
Choose a trained model (e.g., XGBoost, neural network) and a specific prediction instance (single row or batch) that you want to interpret. This step grounds the explanation in a concrete use case, avoiding generic analysis.
Why FiftyOne: FiftyOne is designed for model prediction visualization and dataset curation, directly supporting the selection of a model and prediction to explain by visualizing predictions on a dataset.
Use model-agnostic or model-specific methods (e.g., permutation importance, SHAP summary plot, or built-in feature_importances_) to rank features by their overall contribution to model predictions. This provides a high-level view before diving into local explanations.
Why Captum: Captum is specifically designed for attributing feature importance in PyTorch models, directly matching the need for SHAP-like global feature importance computation.
Apply a local interpretability method (e.g., LIME, SHAP force plot, or Integrated Gradients) to explain why the model made that specific prediction. This step produces a human-readable breakdown of feature contributions for the instance.
Why Captum: Captum supports local explanation methods (e.g., Integrated Gradients, DeepLIFT) for PyTorch models, aligning with LIME/SHAP-style local explanations in a Jupyter notebook.
Check that the explanation is faithful to the model by comparing the sum of feature contributions to the actual prediction, and by testing with a known simple model (e.g., linear regression) as a sanity check. This ensures the explanation is not misleading.
Why Captum: Captum provides fidelity metrics and perturbation-based validation for explanations, directly supporting validation with NumPy and model prediction functions.
Translate the technical explanation into a non-technical summary (e.g., 'The model denied the loan because income was low and debt ratio high') and create a visual report (e.g., PDF or dashboard) with key plots and plain-language takeaways.
Why Mistral AI Models: Mistral AI Models can generate natural language summaries and reports of findings, which can be combined with matplotlib charts for stakeholder communication.
If the explanation reveals unexpected or undesirable feature dependencies (e.g., reliance on a protected attribute), use the insights to retrain or constrain the model. This step closes the feedback loop from explainability to model quality.
Why Tecton: Tecton specializes in feature engineering for ML, directly supporting the feature engineering tools needed for model improvement iteration.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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