## Modelling if condition of multiple estimators in a pipeline

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How to correctly model a if condition to choose estimator/predictor(linear regression, gbt) to be used in scikit/spark-ml in a single pipeline.

if feature_x < constant:
result = pipeline1.predict(feature_vector)
else:
result = pipeline2.predict(feature_vector)


Other than modelling it as custom transformer/predictor, is there a alternate way to model it in a pipeline