Principle:Kubeflow Pipelines XGBoost Model Prediction

Sources	Domains	Last Updated
XGBoost, XGBoost	Machine_Learning, Inference	2026-02-13

Overview

The process of applying a trained XGBoost model to new data to generate predictions.

Description

Model prediction (inference) applies a trained model to input data to produce output predictions. In the pipeline context, the prediction component takes a trained XGBoost model artifact and input CSV data, applies the model, and outputs predictions in CSV format. The label_column parameter identifies which column contains ground truth (for comparison), though the prediction itself does not require labels.

Usage

Use after training to generate predictions on test or validation data for evaluation or deployment.

Theoretical Basis

Ensemble inference — each tree in the boosted ensemble produces a partial prediction; the final output is the sum of all tree predictions.

Related Pages

• Implementation:Kubeflow_Pipelines_XGBoost_Predict_On_CSV_Op