gbm

An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway. Newer version available at github.com/gbm-developers/gbm3.

Last 30 days

This package has been downloaded 27,344 times in the last 30 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 833 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 312,211 times in the last 365 days. That's a whole lot of downloads. Somewhere, a librarian is trying to figure out why more bandwidth is needed. The day with the most downloads was Jul 12, 2024 with 1,832 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.