tda-representation-diagnostics

You can check embedding stability and representation drift by running diagnostic scripts that compare embeddings across runs and detect temporal changes in data representations, ensuring long-term reliability.

Validate UMAP or t-SNE projections by diagnosing scaler, PCA, and loadings behavior alongside observed and null coordinate-frame alignment to ensure your dimensionality reduction preserves meaningful variance.

Yes, you can generate SHAP explanations to provide associational feature importance diagnostics for TDL datasets, though these diagnostics do not imply causation between features and outcomes.

Yes, diagnosing PCA loadings behavior and UMAP projections requires specific Python libraries including scikit-learn, umap-learn, and shap to run the statistical diagnostics and visualizations.

Coordinate frame alignment ensures observed and null data share a common fitted frame, which is critical because misaligned frames can distort UMAP or t-SNE projections and compromise representation accuracy.

SHAP explanations in representation diagnostics provide associational insights only and explicitly avoid causation claims, meaning they can identify feature correlations but cannot prove causal relationships in the data.