3 | | I am using '''Ranger''' a fast implementation of the random forest machine learning algorithm. Quoting from their documentation |
4 | | {{{ |
5 | | ranger is a fast implementation of random forests (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, and survival forests are supported. Classification and regression forests are implemented as in the original Random Forest (Breiman 2001), survival forests as in Random Survival Forests (Ishwaran et al. 2008). Includes implementations of extremely randomized trees (Geurts et al. 2006) and quantile regression forests (Meinshausen 2006). |
6 | | }}} |
7 | | |
8 | | |
9 | | The source code can be grabbed from here: https://github.com/imbs-hl/ranger or cloned from !GitHub via |
| 3 | I am using '''Ranger''' a fast implementation of the random forest machine learning algorithm. The documentation and the paper can be found here: https://doi.org/10.18637/jss.v077.i01. |
| 4 | |
| 5 | Quoting from their documentation |
| 6 | |
| 7 | ```ranger is a fast implementation of random forests (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, and survival forests are supported. Classification and regression forests are implemented as in the original Random Forest (Breiman 2001), survival forests as in Random Survival Forests (Ishwaran et al. 2008). Includes implementations of extremely randomized trees (Geurts et al. 2006) and quantile regression forests (Meinshausen 2006).``` |
| 8 | |
| 9 | |
| 10 | The source code can be grabbed from here: https://github.com/imbs-hl/ranger (where also some additional information are available) or cloned from !GitHub via |
| 11 | |