Evaluation assesses model performance using a test set (unseen data). Metrics include accuracy, precision, recall, F1 score for classification, and Mean Squared Error (MSE) or R-squared for regression. The goal is to ensure the model generalizes well and avoids overfitting.
This is the text view of an interactive 3D knowledge graph — open this page with JavaScript enabled to explore it visually.
🧠 Knowledge Graph
👁 read-only snapshot
Select a node
The owner's editing tools — shown here so you can see how the graph is grown, but read-only.
Click a bubble to drill in · click again to collapse · drag to orbit
Quiz
Proposed changes
⚠️ Includes destructive changes (merge/delete). They can still be reverted with Undo after applying.
🔒 Only the owner can edit this graph
You're browsing a read-only snapshot. The editing tools are shown so you can see how the graph is grown, but changes are disabled here — exploring, searching and path-finding all work.