catboost.staged_predict
catboost.staged_predict(model,
pool,
verbose=FALSE,
prediction_type=None,
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 prediction results will be correct only if the features data in the pool parameter contains all the features used in the model. Typically, the order of these features must match the order of the corresponding columns that is provided during the training. But if feature names are provided both during the training and in the pool parameter when applying the model, they can be matched by names instead of the columns order.
Arguments
model
Description
The model obtained as the result of training.
Default value
Required argument
pool
Description
The input dataset.
Default value
Required argument
verbose
Description
Verbose output to stdout.
Default value
FALSE (not used)
prediction_type
Description
The required prediction type.
Supported prediction types:
- Probability
- Class
- RawFormulaVal
- Exponent
- LogProbability
Default value
None (Exponent for Poisson and Tweedie, RawFormulaVal for all other loss functions)
ntree_start
Description
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 toeval_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.
Default value
0
ntree_end
Description
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 toeval_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.
Default value
0 (the index of the last tree to use equals to the number of trees in the
model minus one)
eval_period
Description
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 toeval_period.
This parameter defines the step to iterate over the range [ntree_start; ntree_end). For example, let's assume that the following parameter values are set:
ntree_startis set 0ntree_endis set to N (the total tree count)eval_periodis set to 2
In this case, the results are returned for the following tree ranges: [0, 2), [0, 4), ... , [0, N).
Default value
1 (the trees are applied sequentially: the first tree, then the first two
trees, etc.)
thread_count
Description
The number of threads to use for operation.
Optimizes the speed of execution. This parameter doesn't affect results.
Default value
-1 (the number of threads is equal to the number of processor cores)