Description Usage Arguments Details Value See Also Examples

View source: R/Xhrf_autotune_gpp.R

`X_RF_autotune_gpp`

will first go through 11 example setups
which have proven to be very good parameters in some cases we have studied
before. After that 'init_points' many points completely at random and
evaluates those. After that it uses the previous observations to initialize
a gaussian process prior and it makes n_iter many updates using this GP
potimization

1 2 | ```
X_RF_autotune_gpp(feat, tr, yobs, ntree = 2000, init_points = 20,
n_iter = 100, nthread = 0, verbose = TRUE, ...)
``` |

`feat` |
A data frame of all the features. |

`tr` |
A numeric vector contain 0 for control and 1 for treated variables. |

`yobs` |
A numeric vector containing the observed outcomes. |

`ntree` |
Number of trees for each of the base learners. |

`init_points` |
Number of completely randomly selected tuning settings. |

`n_iter` |
Number of updates updates to optimize the GPP. |

`nthread` |
Number of threads used. Set it is 0, to automatically select the maximum amount of possible threads. Set it 1 for slowest performance but absolute deterministic behavior. |

This function uses the rBayesianOptimization package to do the baysian optimization

A tuned X learner object.

`X_RF_autotune_simple`

,
`X_RF_autotune_hyperband`

,

1 2 3 4 5 6 7 8 9 10 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.