predict_proba

Apply the model to the given dataset to predict the probability that the object belongs to the given classes.

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

Method call format

predict_proba(data, 
              ntree_start=0, 
              ntree_end=0, 
              thread_count=-1, 
              verbose=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
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)`. 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)`. 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	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
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)`. 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)`. 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	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

numpy.array