Predicted values based on class `lgb.Booster`

# S3 method for lgb.Booster predict(object, data, num_iteration = NULL, rawscore = FALSE, predleaf = FALSE, predcontrib = FALSE, header = FALSE, reshape = FALSE, ...)

object | Object of class |
---|---|

data | a |

num_iteration | number of iteration want to predict with, NULL or <= 0 means use best iteration |

rawscore | whether the prediction should be returned in the for of original untransformed
sum of predictions from boosting iterations' results. E.g., setting |

predleaf | whether predict leaf index instead. |

predcontrib | return per-feature contributions for each record. |

header | only used for prediction for text file. True if text file has header |

reshape | whether to reshape the vector of predictions to a matrix form when there are several prediction outputs per case. |

... | Additional named arguments passed to the |

For regression or binary classification, it returns a vector of length `nrows(data)`

.
For multiclass classification, either a `num_class * nrows(data)`

vector or
a `(nrows(data), num_class)`

dimension matrix is returned, depending on
the `reshape`

value.

When `predleaf = TRUE`

, the output is a matrix object with the
number of columns corresponding to the number of trees.

library(lightgbm) data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) params <- list(objective = "regression", metric = "l2") valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 10L , valids = valids , min_data = 1L , learning_rate = 1.0 , early_stopping_rounds = 5L )#> [1]: test's l2:6.44165e-17 #> [2]: test's l2:6.44165e-17 #> [3]: test's l2:6.44165e-17 #> [4]: test's l2:6.44165e-17 #> [5]: test's l2:6.44165e-17 #> [6]: test's l2:6.44165e-17