---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: e85d638bc8dcbf4b3efb146b057376a4ec99bf199c03fb58ad1153c705bc294f
  pageDirectory: concepts
  sources:
    - distributed-training-of-xgboost-models-using-scala-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - xgboost-regression-with-cross-validation
    - XRWC
    - xgboost-regression-with-cross-validation-notebook
    - XRWCN
  citations:
    - file: distributed-training-of-xgboost-models-using-scala-databricks-on-aws.md
title: XGBoost Regression with Cross-Validation
description: Example notebook demonstrating XGBoost regression with MLlib cross-validation on Databricks
tags:
  - machine-learning
  - regression
  - cross-validation
  - xgboost
timestamp: "2026-06-18T12:04:55.727Z"
---

# XGBoost Regression with Cross-Validation

**XGBoost Regression with Cross-Validation** is a machine learning technique that combines the [XGBoost](/concepts/xgboostspark-module.md) gradient boosting algorithm with MLlib cross-validation to tune hyperparameters and evaluate model performance for regression tasks. This approach is available in [Databricks Runtime ML](/concepts/databricks-runtime-ml.md) and can be implemented using Scala notebooks. ^[distributed-training-of-xgboost-models-using-scala-databricks-on-aws.md]

## Overview

Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. When applied to XGBoost regression, it helps select optimal hyperparameters and provides a more robust estimate of model performance than a single train-test split. ^[distributed-training-of-xgboost-models-using-scala-databricks-on-aws.md]

The integration of XGBoost with MLlib's cross-validation framework allows you to embed an XGBoost regression model into an ML Pipeline and tune it using standard MLlib tools. ^[distributed-training-of-xgboost-models-using-scala-databricks-on-aws.md]

## Implementation

### Embedding XGBoost in an ML Pipeline

The first step is to embed an XGBoost regression model into an MLlib ML pipeline. This allows the XGBoost model to benefit from MLlib's pipeline infrastructure, including feature transformers, evaluators, and cross-validation. ^[distributed-training-of-xgboost-models-using-scala-databricks-on-aws.md]

### Cross-Validation with MLlib

MLlib's cross-validation functionality can be used to tune XGBoost regression models. The cross-validation process: ^[distributed-training-of-xgboost-models-using-scala-databricks-on-aws.md]

1. Splits the training data into `k` folds
2. Trains the model on `k-1` folds and evaluates on the held-out fold
3. Repeats this process `k` times, rotating the held-out fold
4. Averages the evaluation metrics across all folds
5. Selects the best hyperparameter combination based on the average performance

### Available Notebooks

Databricks provides example notebooks demonstrating these techniques: ^[distributed-training-of-xgboost-models-using-scala-databricks-on-aws.md]

- **XGBoost classification with ML pipeline notebook** — Shows how to embed an XGBoost model into an MLlib ML pipeline for classification tasks
- **XGBoost regression with cross-validation notebook** — Demonstrates using MLlib cross-validation to tune an XGBoost regression model

## Benefits

Using cross-validation with XGBoost regression provides several advantages: ^[distributed-training-of-xgboost-models-using-scala-databricks-on-aws.md]

- **Reduced overfitting** — Cross-validation evaluates model performance on multiple data subsets, providing a more reliable estimate of generalization error
- **Optimal hyperparameter selection** — Systematic tuning of parameters such as learning rate, maximum depth, and number of estimators
- **Integration with MLlib** — Leverages Databricks' distributed computing capabilities for scalable model training and tuning

## Related Concepts

- [XGBoost](/concepts/xgboostspark-module.md) — The gradient boosting framework used for regression and classification
- MLlib — Apache Spark's scalable machine learning library
- ML Pipeline — MLlib's API for creating and tuning machine learning workflows
- [Databricks Runtime ML](/concepts/databricks-runtime-ml.md) — The Databricks runtime environment optimized for machine learning workloads
- [Hyperparameter Tuning](/concepts/hyperparameter-tuning.md) — The process of selecting optimal model parameters
- Model Evaluation — Techniques for assessing model performance

## Sources

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

# Citations

1. [distributed-training-of-xgboost-models-using-scala-databricks-on-aws.md](/references/distributed-training-of-xgboost-models-using-scala-databricks-on-aws-760f6d64.md)
