The use of high-quality libraries speeds initial development, results in fewer bugs, reduces reinvention-of-the-wheel scenarios, and cuts long-term maintenance costs. Given that machine learning is inherently experimental in nature, libraries enable fast and maintainable experiments.
The goals of this chapter are to
Introduce ML-Ensemble, a Python-based open source library that wraps scikit ensemble classes to offer a high-level API.
Scale XGBoost via Dask, a flexible library for parallel computing in Python. Dask and XGBoost can work together ...
