R: Predict method for LightGBM model

predict.lgb.Booster {lightgbm} | R Documentation

## Predict method for LightGBM model

### Description

Predicted values based on class `lgb.Booster`

### Usage

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

### Arguments

`object` | Object of class `lgb.Booster`
`data` | a `matrix` object, a `dgCMatrix` object or a character representing a filename
`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 `rawscore=TRUE` for logistic regression would result in predictions for log-odds instead of probabilities.
`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 `predict()` method of the `lgb.Booster` object passed to `object`.

### Value

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.

### Examples

    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,
                       dtrain,
                       100,
                       valids,
                       min_data = 1,
                       learning_rate = 1,
                       early_stopping_rounds = 10)
    preds <- predict(model, test$data)

[Package lightgbm version 2.2.3 Index]