XGBoostSub_bin {BioPred} | R Documentation |

## XGBoost Model with Modified Loss Function for Subgroup Identification with Binary Outcomes

### Description

Function for training XGBoost model with customized loss function for binary outcomes

### Usage

```
XGBoostSub_bin(
X_data,
y_data,
trt,
pi,
Loss_type = "A_learning",
params = list(),
nrounds = 50,
disable_default_eval_metric = 1,
verbose = TRUE
)
```

### Arguments

`X_data` |
The input features matrix. |

`y_data` |
The input y matrix. |

`trt` |
The treatment indicator vector. Should take values of 1 or -1, where 1 represents the treatment group and -1 represents the control group. |

`pi` |
The propensity scores vector, which should range from 0 to 1, representing the probability of assignment to treatment. |

`Loss_type` |
Type of loss function to use: "A_learning" or "Weight_learning". |

`params` |
A list of additional parameters for the xgb.train function. |

`nrounds` |
Number of boosting rounds. Default is 50. |

`disable_default_eval_metric` |
If 1, default evaluation metric will be disabled. |

`verbose` |
Logical. If TRUE, training progress will be printed; if FALSE, no progress will be printed. |

### Details

XGBoostSub_bin: Function for Training XGBoost Model with Customized Loss Function for binary outcomes

This function trains an XGBoost model using a customized loss function based on the A-learning and weight-learning.

This function requires the 'xgboost' library. Make sure to install and load the 'xgboost' library before using this function.

After running this function, the returned model can be used like a regular xgboost model.

### Value

Trained XGBoostSub_bin model.

*BioPred*version 1.0.1 Index]