bestEATBoost {boostingDEA} | R Documentation |

## Tuning an EATBoost model

### Description

This function computes the root mean squared error (RMSE) for a set of EATBoost models built with a grid of given hyperparameters.

### Usage

```
bestEATBoost(
training,
test,
x,
y,
num.iterations,
learning.rate,
num.leaves,
verbose = TRUE
)
```

### Arguments

`training` |
Training |

`test` |
Test |

`x` |
Column input indexes in |

`y` |
Column output indexes in |

`num.iterations` |
Maximum number of iterations the algorithm will perform |

`learning.rate` |
Learning rate that control overfitting of the algorithm. Value must be in (0,1] |

`num.leaves` |
Maximum number of terminal leaves in each tree at each iteration |

`verbose` |
Controls the verbosity. |

### Value

A `data.frame`

with the sets of hyperparameters and the root
mean squared error (RMSE) and mean square error (MSE) associated for each
model.

[Package

*boostingDEA*version 0.1.0 Index]