Description Usage Arguments Value

Training a gradient boosting machine

1 2 3 4 5 6 | ```
gbm_train(train_x, train_y, test_x, test_y, pred_method = "2",
n_model = 500, batch_size = 1000, lr = 0.1, decay_lr = 1,
tune_param = FALSE, tune_size = NULL, sr_size = NULL,
selected_n_feature = NULL, update_kparam_tiems = 50,
update_col_sample = 50, update_lr = 50, kname = "gaussiandotrel",
ktheta = NULL, kbetainv = NULL, ncpu = -1)
``` |

`train_x` |
Matrix; the features of training data set. |

`train_y` |
Matrix; y. |

`pred_method` |
String; Set the model training approach. 1: random row sampling after all training data have been used. 2: random row sampling. 3: row sampling plus col sampling. |

`n_model` |
Positive number of submodel in gbm. |

`batch_size` |
Positive integer; batch size for each iteration. |

`lr` |
Numeric between 0-1; learning rate. |

`decay_lr` |
Numeric between 0-1; decay learning rate, default = 1 (no decay). |

`tune_param` |
Boolean; Set to TRUE to tune parameters of kernel function default value is TRUE. |

`tune_size` |
Positve integer; size of tuning data set. |

`sr_size` |
Positive integer; size of sub dataset in gbm_sr |

`update_col_sample` |
Positive integer; time to update kernel parameter(for method 3). |

`update_lr` |
Positive integer; time to decay learning rate; default is 50. |

`kname` |
String; the name of kernel; default value is 'gaussiandotrel'. |

`ktheta` |
Numeric vector; store kernel parameter; should be provided when tune_param is FALSE. |

`kbetainv` |
Numeric; store kernel parameter betainv; shuld be provided when tune_param is FALSE. |

`ncpu` |
Integer; the number of thread to be used; set to -1 to use all threads; default value is -1. |

`tune_param` |
Boolean; Set to TRUE to tune parameters of kernel function, default value for pred_method 1 & 2 is TRUE. default value for pred_method 3 is FALSE. |

`update_kparam_times` |
Positve integer; time to update kernel parameter(for method 1/2). |

return a list having four objects: models pred_method train_rmse test_rmse

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