---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 6834f8535a5ea9d917f34d4ab278c8f194278b4f7dd82d3ed537ae47c7325c7c
  pageDirectory: concepts
  sources:
    - hyperopt-best-practices-and-troubleshooting-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - hyperopt-nan-loss-handling
    - HNLH
  citations:
    - file: hyperopt-best-practices-and-troubleshooting-databricks-on-aws.md
title: Hyperopt NaN Loss Handling
description: A NaN loss from Hyperopt fmin() usually means the objective function returned NaN; it does not affect other trials and can be safely ignored or mitigated by adjusting the search space or objective function.
tags:
  - troubleshooting
  - hyperparameter-optimization
  - error-handling
timestamp: "2026-06-19T19:07:51.233Z"
---

# Hyperopt NaN Loss Handling

**Hyperopt NaN Loss Handling** refers to the behavior and recommended practices when a hyperparameter optimization trial using [Hyperopt](/concepts/hyperopt.md) returns a loss value of NaN (Not a Number). This is a common issue during hyperparameter tuning that does not necessarily indicate a critical failure.

## Overview

When using Hyperopt's `fmin()` function, a reported loss of NaN typically means the objective function passed to `fmin()` returned NaN for that particular trial. This can occur due to numerical instability, invalid hyperparameter combinations, or errors in the model training process for specific parameter configurations. ^[hyperopt-best-practices-and-troubleshooting-databricks-on-aws.md]

## Impact on Optimization

A NaN loss value does not affect other runs in the optimization process. Hyperopt continues evaluating other trials independently, and the NaN result is simply recorded for that specific trial. The overall optimization can proceed normally. ^[hyperopt-best-practices-and-troubleshooting-databricks-on-aws.md]

## Recommended Actions

### Ignoring NaN Losses

In many cases, NaN losses can be safely ignored. Since Hyperopt uses stochastic search algorithms like the Tree of Parzen Estimators (TPE), the loss does not decrease monotonically with each run anyway. A few NaN results are unlikely to significantly impact the overall optimization quality. ^[hyperopt-best-practices-and-troubleshooting-databricks-on-aws.md]

### Preventing NaN Losses

To reduce the occurrence of NaN losses, consider:

- **Adjusting the hyperparameter space**: Restrict ranges for hyperparameters that are prone to causing numerical instability, such as learning rates or regularization parameters.
- **Modifying the objective function**: Add error handling within the objective function to catch exceptions or return a large penalty value instead of NaN.
- **Using domain knowledge**: Restrict the search domain to parameter combinations that are known to be numerically stable. ^[hyperopt-best-practices-and-troubleshooting-databricks-on-aws.md]

## Relationship to Other Hyperopt Concepts

- [Hyperopt TPE Algorithm](/concepts/hyperopt-tree-of-parzen-estimators-tpe-algorithm.md) — The Bayesian optimization algorithm that can handle noisy objective functions, including occasional NaN values.
- [Hyperopt fmin()](/concepts/hyperopt-fmin.md) — The core optimization function where NaN losses may be returned.
- [Hyperopt SparkTrials](/concepts/sparktrials.md) — Distributed execution mode where NaN losses in individual trials do not affect other parallel trials.
- Hyperopt Search Spaces — Properly defining search spaces can help prevent NaN losses.

## Sources

- hyperopt-best-practices-and-troubleshooting-databricks-on-aws.md

# Citations

1. [hyperopt-best-practices-and-troubleshooting-databricks-on-aws.md](/references/hyperopt-best-practices-and-troubleshooting-databricks-on-aws-838e4655.md)
