---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 77593aafac15d22da551e19b6bb20800ebc1497bc683fa9fa985bd46d2371ed4
  pageDirectory: concepts
  sources:
    - distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - sparse-data-optimization-in-xgboostspark
    - SDOIX
  citations:
    - file: distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md
title: Sparse Data Optimization in xgboost.spark
description: Performance optimization for datasets with sparse features by setting enable_sparse_data_optim=True and missing=0.0, requiring SparseVector feature columns.
tags:
  - performance
  - spark
  - xgboost
timestamp: "2026-06-19T18:36:01.053Z"
---

# Sparse Data Optimization in xgboost.spark

**Sparse Data Optimization in xgboost.spark** is a feature that improves training performance when working with high-dimensional datasets where most feature values are zero. The PySpark estimators in the `xgboost.spark` module — `SparkXGBRegressor`, `SparkXGBClassifier`, and `SparkXGBRanker` — support this optimization through a dedicated parameter and require sparse vector input. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

## Enabling Sparse Data Optimization

To enable optimization of sparse feature sets, two conditions must be met:

1. **Sparse vector input**: The dataset passed to the `fit` method must contain a features column consisting of values of type `pyspark.ml.linalg.SparseVector`.
2. **Estimator parameters**: The estimator parameter `enable_sparse_data_optim` must be set to `True`, and the `missing` parameter must be set to `0.0`. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

The `missing=0.0` setting ensures that zero values in the sparse vectors are treated as missing values, which aligns with the typical interpretation of sparse feature representations.

### Example

```python
from xgboost.spark import SparkXGBClassifier

classifier = SparkXGBClassifier(
    enable_sparse_data_optim=True,
    missing=0.0
)
classifier.fit(dataset_with_sparse_features_col)
```

^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

## Benefits

Using sparse data optimization can lead to reduced memory consumption because sparse vectors are stored in a compact format, avoiding allocation for zero-valued entries. Training may also be faster as the XGBoost algorithm can skip computations for features with value zero. This enables handling of larger, higher-dimensional datasets that would be impractical to represent as dense vectors.

## Related Concepts

- [Distributed Training of XGBoost Models](/concepts/distributed-training-with-xgboostspark.md) — Broader context on using `xgboost.spark` with PySpark.
- SparseVector — The data type required for the features column in sparse optimization.
- DMatrix — The internal data structure used by XGBoost, which `xgboost.spark` estimators use to manage memory efficiently.
- [GPU Training with xgboost.spark](/concepts/gpu-training-with-xgboostspark.md) — Using GPUs with `xgboost.spark`, which can be combined with sparse optimization.

## Sources

- distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md

# Citations

1. [distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md](/references/distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws-49a22c88.md)
