Predicted values based on class lgb.Booster
New in version 4.0.0
# S3 method for lgb.Booster predict( object, newdata, type = "response", start_iteration = NULL, num_iteration = NULL, header = FALSE, params = list(), ... )
| object | Object of class |
|---|---|
| newdata | a For sparse inputs, if predictions are only going to be made for a single row, it will be faster to
use CSR format, in which case the data may be passed as either a single-row CSR matrix (class
If single-row predictions are going to be performed frequently, it is recommended to pre-configure the model object for fast single-row sparse predictions through function lgb.configure_fast_predict. Changed from 'data', in version 4.0.0 |
| type | Type of prediction to output. Allowed types are:
Note that, if using custom objectives, types "class" and "response" will not be available and will default towards using "raw" instead. If the model was fit through function lightgbm and it was passed a factor as labels,
passing the prediction type through New in version 4.0.0 |
| start_iteration | int or None, optional (default=None) Start index of the iteration to predict. If None or <= 0, starts from the first iteration. |
| 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). |
| header | only used for prediction for text file. True if text file has header |
| params | a list of additional named parameters. See
the "Predict Parameters" section of the documentation for a list of parameters and
valid values. Where these conflict with the values of keyword arguments to this function,
the values in |
| ... | ignored |
For prediction types that are meant to always return one output per observation (e.g. when predicting
type="response" or type="raw" on a binary classification or regression objective), will
return a vector with one element per row in newdata.
For prediction types that are meant to return more than one output per observation (e.g. when predicting
type="response" or type="raw" on a multi-class objective, or when predicting
type="leaf", regardless of objective), will return a matrix with one row per observation in
newdata and one column per output.
For type="leaf" predictions, will return a matrix with one row per observation in newdata
and one column per tree. Note that for multiclass objectives, LightGBM trains one tree per class at each
boosting iteration. That means that, for example, for a multiclass model with 3 classes, the leaf
predictions for the first class can be found in columns 1, 4, 7, 10, etc.
For type="contrib", will return a matrix of SHAP values with one row per observation in
newdata and columns corresponding to features. For regression, ranking, cross-entropy, and binary
classification objectives, this matrix contains one column per feature plus a final column containing the
Shapley base value. For multiclass objectives, this matrix will represent num_classes such matrices,
in the order "feature contributions for first class, feature contributions for second class, feature
contributions for third class, etc.".
If the model was fit through function lightgbm and it was passed a factor as labels, predictions
returned from this function will retain the factor levels (either as values for type="class", or
as column names for type="response" and type="raw" for multi-class objectives). Note that
passing the requested prediction type under params instead of through type might result in
the factor levels not being present in the output.
If the model object has been configured for fast single-row predictions through lgb.configure_fast_predict, this function will use the prediction parameters that were configured for it - as such, extra prediction parameters should not be passed here, otherwise the configuration will be ignored and the slow route will be taken.
# \donttest{ setLGBMthreads(2L) data.table::setDTthreads(1L) 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" , min_data = 1L , learning_rate = 1.0 , num_threads = 2L ) valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 5L , valids = valids ) #> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000886 seconds. #> You can set `force_row_wise=true` to remove the overhead. #> And if memory is not enough, you can set `force_col_wise=true`. #> [LightGBM] [Info] Total Bins 232 #> [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116 #> [LightGBM] [Info] Start training from score 0.482113 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [1]: test's l2:6.44165e-17 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [2]: test's l2:1.97215e-31 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [3]: test's l2:0 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements #> [4]: test's l2:0 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements #> [5]: test's l2:0 preds <- predict(model, test$data) # pass other prediction parameters preds <- predict( model, test$data, params = list( predict_disable_shape_check = TRUE ) ) # }