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Sklearn Pipeline

Create and use sklearn pipelines chaining transformers and estimators

Actions3

Predict

AI-generated

Summary

Predict outcomes using a previously trained scikit‑learn pipeline

Inputs

  • pipelineData (required) — JSON string containing the trained pipeline (base64‑encoded pickle)
  • predictFeatureColumns (required) — Comma‑separated list of feature column names to use for prediction
  • pythonPath — Path to the Python executable that has scikit‑learn installed (default: 'python3')

Output shape

a list of result objects, each containing prediction results (e.g., 'prediction' and optionally 'probabilities' or transformed fields)

Each item may include 'prediction' (the predicted value); for classification models it may also include 'probabilities'; transformed data is returned as 'transformed_1', 'transformed_2', etc.

Examples

Example 1: Classify iris species

pipelineData = '', predictFeatureColumns = 'sepal_length,sepal_width,petal_length,petal_width', pythonPath = 'python3'

Example 2: Transform data with a pipeline

pipelineData = '', predictFeatureColumns = 'feature1,feature2', pythonPath = 'python3'

Example 3: Predict continuous target

pipelineData = '', predictFeatureColumns = 'age,income,education', pythonPath = '/usr/bin/python3'

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