This week, H2O had a major release of their ML open-source library for #R and #Python, introducing two new algorithms, improvements, and bug fixing. โค๏ธ๐๐ผ ๐งต
New algorithm (1/2):
โจ Distributed Uplift Random Forest (Uplift DRF) - The Uplift DRF is a tree-based algorithm that uses a Random Forecast classifier to estimate a treatment's incremental impact. See demo on the notebook โฌ๏ธ github.com/h2oai/h2o-3/blโฆ #randomforest#ML#UpLift
New algorithm (2/2):
โจ Infogram & Admissible Machine Learning - is a new tool for machine learning interpretability. More details are available on the algorithm doc โฌ๏ธ h2o.ai/blog/h2o-releaโฆ #machinelearning#ML
This release also includes different improvements in:
โจAutoML
โจRuleFit
โจSparkling Water
โจSupport of Java 17
The library uses under the hood probabilistic programming languages with libraries such as #Python@mcmc_stan , Pyro, and #PyTorch to build the forecast estimators.
The new release, version 1.1 includes the following new features and changes: ๐๐ผ
Did you know that An Introduction to Statistical Learning (ISLR) book has an online course? ๐ฅ ๐โค๏ธ @edXOnline is offering an online course by @Stanford University, following the book curriculum: ๐งต ๐๐ผ
The course instructors are two of the book authors - Prof. Trevor Hastie and Prof. @robtibshirani. While the book is based on #R some awesome people translate it to #python, #julialang, and other #Rstats flavors (see links on the comments below ๐๐ผ).
The course covers the following topics (aligned with the book curriculum):
New book for Bayesian statistics with #Python! ๐๐๐
The Bayesian Modeling and Computation in Python by @aloctavodia, @canyon289, and @junpenglao provides an introduction to Bayesian statistics using core Python libraries for Bayesian ๐งต ๐๐ผ
The book covers the following four topics:
- Bayesian Inference concepts
- Bayesian regression methods for linear regressions, splines,
- Time series #forecasting
- Bayesian additive regression trees
- Approximate of Bayesian computation