XGBoost vs Aquarium: What are the differences?

Developers describe XGBoost 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. On the other hand, Aquarium is detailed as "*Improve Your ML Dataset Quality *". Machine learning models are only as good as the datasets they're trained on It helps ML teams make better models by improving their dataset quality..

XGBoost can be classified as a tool in the "Python Build Tools" category, while Aquarium is grouped under "Machine Learning Tools".

Some of the features offered by XGBoost are:

• Flexible
• Portable
• Multiple Languages

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

• Upload your dataset to get a health check of its quality, quantity, and diversity. Zoom in and out of your dataset. Uncover distribution biases before you train. Find and fix labeling errors quickly
• Upload model inferences against your labeled datasets and deep dive into its performance. Find where your model is performing well and badly so you can take the best actions to improve it
• With knowledge of your dataset diversity and model performance, it automatically samples the best data to sample to label and retrain on. Your model performance just gets better

XGBoost is an open source tool with 19.4K GitHub stars and 7.62K GitHub forks. Here's a link to XGBoost's open source repository on GitHub.