> ## Documentation Index
> Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Baselines
## **Baseline Evaluation Metrics**
On your **training job's evaluation page**, Kumo provides **comparisons of your model versus heuristic baselines** for **temporal binary classification, link prediction, and regression tasks**. These baselines allow you to compare the results of your **Kumo model** against **simple heuristic-based approaches**.
Baselines are computed on a **per-entity level**, meaning:
* **Regression tasks** use **past labels as forecasts**.
* **Link prediction tasks** recommend **past user behavior** as future predictions.
Baselines are not supported when `timeframe_step` is `None`.
***
## **Baselines Per Task Type**
The following table outlines **current baselines** supported by Kumo, organized by **prediction task type**:
| Task Type | Temporal Problem |
| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Link Prediction | Per Entity Frequency – Predicting each entity with the most popular ranked list of target values from all previous splits. |
| Regression | Per Entity Mean (Personalized Average) – Predicting each entity with the average of all past target values from previous splits. Per Entity Last Label – Predicting each entity using the **most recent past value** as the forecast. |
| Binary Classification | Per Entity Mean – Predicting each entity with the average of all target values from previous splits. Per Entity Last Label – Predicting each entity with the previous time frame’s label |
### **Why Use Baselines?**
Baseline models provide a **reference point** to determine how much your Kumo-trained model **improves over simple heuristics**. A well-performing model should **outperform these baselines** significantly.