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H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

  • Updated Apr 8, 2022
  • Jupyter Notebook
awesome-decision-tree-papers
mljar-supervised
moshe-rl
moshe-rl commented Nov 30, 2021

When using r2 as eval metric for regression task (with 'Explain' mode) the metric values reported in Leaderboard (at README.md file) are multiplied by -1.
For instance, the metric value for some model shown in the Leaderboard is -0.41, while when clicking the model name leads to the detailed results page - and there the value of r2 is 0.41.
I've noticed that when one of R2 metric values in the L

bug help wanted good first issue
MichalChromcak
MichalChromcak commented Apr 1, 2022

I published a new v0.1.12 release of HCrystalBall, that updated some package dependencies and fixed some bugs in cross validation.

Should the original pin for 0.1.10 be updated? Unfortunately won't have time soon to submit a PR for this.

good first issue dependencies
awesome-fraud-detection-papers
awesome-gradient-boosting-papers
xuyxu
xuyxu commented Feb 12, 2021

Thanks to the contributors, many new features have been developed. As a result, the current version of documentation could be ambiguous, and requires more explanation or demonstration.

This issue collects suggestions on the documentation. Any one is welcomed to improve the readability of the documentation. For contributors unfamiliar with our workflow on building the documentation, please refe

good first issue
tibshirani
tibshirani commented Sep 5, 2018

I ran a regression_forest for > 10 minutes and had no idea if it would complete in 15 min or an hour.

It would be great to have an argument "verbose" (default FALSE) which causes the function to
print the function's progress, to help the user estimate the remaining time before completion.

feature good first issue

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

  • Updated Mar 23, 2022
  • Python

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