AutoMLPipeline vs XGBoost

AutoMLPipeline vs XGBoost: What are the differences?

Developers describe AutoMLPipeline as "A package that makes it trivial to create and evaluate machine learning pipeline architectures (by IBM)". It is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and automatically discover optimal structures for machine learning prediction and classification. On the other hand, XGBoost is detailed as "Scalable and Flexible Gradient Boosting". Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow.

AutoMLPipeline and XGBoost are primarily classified as "Machine Learning" and "Python Build" tools respectively.

Some of the features offered by AutoMLPipeline are:

• Pipeline API that allows high-level description of processing workflow
• Common API wrappers for ML libs including Scikitlearn, DecisionTree, etc
• Symbolic pipeline parsing for easy expression of complexed pipeline structures

On the other hand, XGBoost provides the following key features:

• Flexible
• Portable
• Multiple Languages