What is the primary purpose of the Predictive Model Markup Language (PMML)?

The primary purpose of the Predictive Model Markup Language (PMML) is to represent predictive models for sharing between applications. PMML is an XML-based language that enables interoperability between different data mining and predictive modeling tools. By standardizing the way models are represented, PMML allows different applications to share models without needing to re-implement the underlying algorithms or code. This capability simplifies the deployment of models across various environments and ensures consistency in how predictions are made and interpreted.

The language supports a wide variety of model types, including decision trees, neural networks, support vector machines, and many others, providing a universal format that data scientists and developers can use effectively. This sharing capability is crucial in a multi-tool ecosystem, as it streamlines processes and enhances collaboration among teams in data science projects.