catboost.predict

catboost.predict(model, 
                 pool, 
                 verbose = FALSE,
                 prediction_type = "RawFormulaVal", 
                 ntree_start = 0, 
                 ntree_end = 0, 
                 thread_count = -1 (the number of threads is equal to the number of cores))

Purpose

Apply the model to the given dataset.

Note. The model can not be correctly applied if the order of the columns in the testing and training datasets differs.

Arguments

ArgumentDescriptionDefault value
model

The model obtained as the result of training.

Required argument
pool

The input dataset.

Required argument
verboseVerbose output to stdout.FALSE (not used)
prediction_type

The required prediction type.

Supported prediction types:
  • Probability
  • Class
  • RawFormulaVal
RawFormulaVal
ntree_start

To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to [ntree_start; ntree_end).

This parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based.

0
ntree_end

To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to [ntree_start; ntree_end).

This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based.

0 (the index of the last tree to use equals to the number of trees in the model minus one)
thread_count

The number of threads to use during training.

Optimizes the speed of execution. This parameter doesn't affect results.

-1 (the number of threads is equal to the number of cores)

Specifics

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.