Implementation:Kubeflow Pipelines Regression Metrics Evaluation
Sources: Kubeflow Pipelines
Domains: Machine_Learning, Evaluation
Last Updated: 2026-02-13
Overview
Wrapper Doc for the reusable KFP component that calculates regression metrics from CSV predictions and ground truth.
Description
calculate_regression_metrics_from_csv_op is loaded from a remote YAML spec. It compares true values against predicted values and outputs multiple regression metrics including mean_squared_error.
Code Reference
Source: samples/core/train_until_good/train_until_good.py (L28 loading, L52-55 invocation)
Import: from kfp import components
calculate_regression_metrics_from_csv_op = components.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/.../components/ml_metrics/Calculate_regression_metrics/from_CSV/component.yaml'
)
metrics_task = calculate_regression_metrics_from_csv_op(
true_values=true_values,
predicted_values=predictions,
)
# Access outputs
mse = metrics_task.outputs['mean_squared_error']
I/O Contract
| Name | Type | Required | Description |
|---|---|---|---|
| true_values | CSV | Yes | Ground truth values |
| predicted_values | CSV | Yes | Model predictions |
| Name | Type | Description |
|---|---|---|
| mean_squared_error | float | Mean squared error between predictions and ground truth |
| mean_absolute_error | float | Mean absolute error between predictions and ground truth |
| root_mean_squared_error | float | Root mean squared error between predictions and ground truth |
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