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 27,842 times in the last 30 days. The downloads are officially high enough to crash an underfunded departmental server. Quite an accomplishment! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 1,111 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 312,953 times in the last 365 days. This is the kind of download count that makes grant committees nod approvingly. A job well done, even the stoic reviewers might be impressed! 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.