predict.lgb.Booster.Rd 2.88 KB
Newer Older
Guolin Ke's avatar
Guolin Ke committed
1
2
3
4
5
6
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lgb.Booster.R
\name{predict.lgb.Booster}
\alias{predict.lgb.Booster}
\title{Predict method for LightGBM model}
\usage{
7
8
\method{predict}{lgb.Booster}(
  object,
9
  newdata,
10
  start_iteration = NULL,
11
12
13
14
15
  num_iteration = NULL,
  rawscore = FALSE,
  predleaf = FALSE,
  predcontrib = FALSE,
  header = FALSE,
16
  params = list(),
17
18
  ...
)
Guolin Ke's avatar
Guolin Ke committed
19
20
21
22
}
\arguments{
\item{object}{Object of class \code{lgb.Booster}}

23
\item{newdata}{a \code{matrix} object, a \code{dgCMatrix} object or
24
a character representing a path to a text file (CSV, TSV, or LibSVM)}
Guolin Ke's avatar
Guolin Ke committed
25

26
27
28
29
30
31
32
33
34
\item{start_iteration}{int or None, optional (default=None)
Start index of the iteration to predict.
If None or <= 0, starts from the first iteration.}

\item{num_iteration}{int or None, optional (default=None)
Limit number of iterations in the prediction.
If None, if the best iteration exists and start_iteration is None or <= 0, the
best iteration is used; otherwise, all iterations from start_iteration are used.
If <= 0, all iterations from start_iteration are used (no limits).}
Guolin Ke's avatar
Guolin Ke committed
35
36

\item{rawscore}{whether the prediction should be returned in the for of original untransformed
37
38
sum of predictions from boosting iterations' results. E.g., setting \code{rawscore=TRUE}
for logistic regression would result in predictions for log-odds instead of probabilities.}
Guolin Ke's avatar
Guolin Ke committed
39
40
41

\item{predleaf}{whether predict leaf index instead.}

James Lamb's avatar
James Lamb committed
42
43
\item{predcontrib}{return per-feature contributions for each record.}

Guolin Ke's avatar
Guolin Ke committed
44
45
\item{header}{only used for prediction for text file. True if text file has header}

46
47
48
49
50
\item{params}{a list of additional named parameters. See
\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{
the "Predict Parameters" section of the documentation} for a list of parameters and
valid values.}

51
\item{...}{ignored}
Guolin Ke's avatar
Guolin Ke committed
52
53
54
}
\value{
For regression or binary classification, it returns a vector of length \code{nrows(data)}.
55
        For multiclass classification, it returns a matrix of dimensions \code{(nrows(data), num_class)}.
Guolin Ke's avatar
Guolin Ke committed
56

57
58
        When passing \code{predleaf=TRUE} or \code{predcontrib=TRUE}, the output will always be
        returned as a matrix.
Guolin Ke's avatar
Guolin Ke committed
59
60
61
62
63
}
\description{
Predicted values based on class \code{lgb.Booster}
}
\examples{
64
\donttest{
Guolin Ke's avatar
Guolin Ke committed
65
66
67
68
69
70
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)
71
72
73
74
75
76
params <- list(
  objective = "regression"
  , metric = "l2"
  , min_data = 1L
  , learning_rate = 1.0
)
Guolin Ke's avatar
Guolin Ke committed
77
valids <- list(test = dtest)
78
79
80
model <- lgb.train(
  params = params
  , data = dtrain
81
  , nrounds = 5L
82
83
  , valids = valids
)
Guolin Ke's avatar
Guolin Ke committed
84
preds <- predict(model, test$data)
85
86

# pass other prediction parameters
87
preds <- predict(
88
89
90
91
92
93
    model,
    test$data,
    params = list(
        predict_disable_shape_check = TRUE
   )
)
Guolin Ke's avatar
Guolin Ke committed
94
}
95
}