staged_predict

Apply the model to the given dataset and calculate the results for each i-th tree of the model taking into consideration only the trees in the range [1;i].

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

Method call format

staged_predict(data,
    prediction_type='RawFormulaVal',
    ntree_start=0, 
    ntree_end=0, 
    eval_period=1, 
    thread_count=-1,
    verbose=None)

Parameters


Parameter	Possible types	Description	Default value
data	catboost.Pool list of lists numpy.array of shape `(doc_count, feature_count)` pandas.DataFrame pandas.Series catboost.FeaturesData	A file or matrix with the input dataset.	Required parameter
prediction_type	string	The required prediction type. Supported prediction types: Probability Class RawFormulaVal	RawFormulaVal
ntree_start	int	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	int	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	int	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 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	int	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) (The number of processor cores)
verbose	bool	Output the measured evaluation metric to stderr.	None


Parameter	Possible types	Description	Default value
data	catboost.Pool list of lists numpy.array of shape `(doc_count, feature_count)` pandas.DataFrame pandas.Series catboost.FeaturesData	A file or matrix with the input dataset.	Required parameter
prediction_type	string	The required prediction type. Supported prediction types: Probability Class RawFormulaVal	RawFormulaVal
ntree_start	int	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	int	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	int	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 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	int	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) (The number of processor cores)
verbose	bool	Output the measured evaluation metric to stderr.	None

Type of return value

generator with numpy.array for each iteration.