XGBoost vs Manifold: What are the differences?

XGBoost: 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; Manifold: A model-agnostic visual debugging tool for machine learning. Understanding ML model performance and behavior is a non-trivial process, given the intrisic opacity of ML algorithms. Performance summary statistics such as AUC, RMSE, and others are not instructive enough for identifying what went wrong with a model or how to improve it. As a visual analytics tool, Manifold allows ML practitioners to look beyond overall summary metrics to detect which subset of data a model is inaccurately predicting.

XGBoost belongs to "Python Build Tools" category of the tech stack, while Manifold can be primarily classified under "Machine Learning Tools".

Some of the features offered by XGBoost are:

• Flexible
• Portable
• Multiple Languages

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

• Performance Comparison View
• Feature Attribution View
• Histogram / heatmap

Manifold is an open source tool with 778 GitHub stars and 58 GitHub forks. Here's a link to Manifold's open source repository on GitHub.