catboost.staged_predict

catboost.staged_predict(model, 
                        pool, 
                        verbose = FALSE, 
                        prediction_type = "RawFormulaVal", 
                        ntree_start = 0, 
                        ntree_end = 0, 
                        eval_period = 1, 
                        thread_count = -1)

Purpose

Apply the model to the given dataset and calculate the results for the specified trees only.

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

Arguments


Argument	Description	Default value
model	The model obtained as the result of training.	Required argument
pool	The input dataset.	Required argument
verbose	Verbose 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)` and the the step of the trees to use to `eval_period`. 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)` and the the step of the trees to use to `eval_period`. 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)
eval_period	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)` and the the step of the trees to use to `eval_period`. This parameter defines the step of the trees to use for the staged prediction mode. In this mode the results for the `(ni)`-th tree of the model are calculated taking into consideration only the trees in the range `[ntree_start; ntree_start + ni]`. The specified value `n` defines the size of the range of trees to use. The approximate values for the last period are calculated using all trees in the provided segment.	1 (the trees are applied sequentially: the first tree, then the first two trees, etc.)
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 processor cores)


Argument	Description	Default value
model	The model obtained as the result of training.	Required argument
pool	The input dataset.	Required argument
verbose	Verbose 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)` and the the step of the trees to use to `eval_period`. 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)` and the the step of the trees to use to `eval_period`. 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)
eval_period	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)` and the the step of the trees to use to `eval_period`. This parameter defines the step of the trees to use for the staged prediction mode. In this mode the results for the `(ni)`-th tree of the model are calculated taking into consideration only the trees in the range `[ntree_start; ntree_start + ni]`. The specified value `n` defines the size of the range of trees to use. The approximate values for the last period are calculated using all trees in the provided segment.	1 (the trees are applied sequentially: the first tree, then the first two trees, etc.)
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 processor cores)