# Model evaluation and metrics
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/index.html

> Model evaluation and metrics accelerators that you can add to your experiment workflow.

# Model evaluation and metrics

| Topic | Description |
| --- | --- |
| Custom metrics for model selection | Demonstrates how to leverage DataRobot's Python client to extract predictions, compute custom metrics, and sort DataRobot models accordingly. |
| t-SNE dimensionality reduction | Learn how to use t-SNE for dimensionality reduction and visualization of high-dimensional data, with examples for exporting these insights as files and plots. |
| Monitor generative AI metrics | Monitor LLMs and generative AI solutions to measure alignment, return on investment, and provide guardrails using custom metrics. |
| Event log viewer | Change the output of the User Activity Monitor to drop or anonymize columns for privacy while maintaining reporting consistency. |
| LLM observability | Enable LLMOps or Observability in your existing Generative AI Solutions without refactoring code, with examples for major LLMs. |
| Partial dependence plots (PDP/ICE) | Create one-way and two-way partial dependence plots (PDP), and Individual Conditional Expectations (ICE) insights using DataRobot. |
| LIME explanations for models | Apply Local Interpretable Model-agnostic Explanations (LIME) to models built and deployed with DataRobot. |
| Steel defect detection | Train a highly accurate and robust machine learning model capable of detecting and classifying any-sized scratch present in steel plates. |
| Export model insights | Review examples for exporting a variety of DataRobot model insights and performance metrics as both machine-readable files and plots in multiple formats. |

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