| catboost.predict {catboost} | R Documentation |
Apply the model to the given dataset.
catboost.predict(model, pool, verbose = FALSE, prediction_type = "RawFormulaVal", ntree_start = 0, ntree_end = 0, thread_count = -1)
model |
The model obtained as the result of training. Default value: Required argument |
pool |
The input dataset. Default value: Required argument |
verbose |
Verbose output to stdout. Default value: FALSE (not used) |
prediction_type |
The format for displaying approximated values in output data (see https://tech.yandex.com/catboost/doc/dg/concepts/output-data-docpage/#output-data). Possible values:
Default value: 'RawFormulaVal' |
ntree_start |
Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). Default value: 0 |
ntree_end |
Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). Default value: 0 (if value equals to 0 this parameter is ignored and ntree_end equal to tree_count) |
thread_count |
The number of threads to use when applying the model. If -1, then the number of threads is set to the number of CPU cores. Allows you to optimize the speed of execution. This parameter doesn't affect results. Default value: 1 |
Peculiarities: In case of multiclassification the prediction is returned in the form of a matrix. Each line of this matrix contains the predictions for one object of the input dataset.
https://tech.yandex.com/catboost/doc/dg/concepts/r-reference_catboost-predict-docpage/