staged_predict_proba
Apply the model to the given dataset to predict the probability that the object belongs to the class and calculate the results taking into consideration only the trees in the range [0; i).
Note
The model prediction results will be correct only if the data parameter with feature values 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 when applying the model, they can be matched by names instead of columns order. Feature names can be specified if the data parameter has one of the following types:
- FeaturesData
- catboost.Pool
- pandas.DataFrame (in this case, feature names are taken from column names)
Method call format
staged_predict_proba(data,
ntree_start=0,
ntree_end=0,
eval_period=1,
thread_count=-1,
verbose=None)
Parameters
data
Description
Feature values data.
The format depends on the number of input objects:
- Multiple — Matrix-like data of shape
(object_count, feature_count) - Single — An array
Possible types
For multiple objects:
- catboost.Pool
- list of lists
- numpy.ndarray of shape
(object_count, feature_count) - pandas.DataFrame
- pandas.SparseDataFrame
- pandas.Series
- catboost.FeaturesData
-
scipy.sparse.spmatrix (all subclasses except dia_matrix)
For a single object:
- list of feature values
- one-dimensional numpy.ndarray with feature values
Default value
Required parameter
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 eval_period parameter to k to calculate metrics on every k-th iteration.
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.
Possible types
int
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 eval_period parameter to k to calculate metrics on every k-th iteration.
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.
Possible types
int
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 eval_period parameter to k to calculate metrics on every k-th iteration.
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 metrics are calculated for the following tree ranges: [0, 2), [0, 4), ... , [0, N)
Possible types
int
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.
{% include reusage-thread_count__cpu_cores__optimizes-the-speed-of-execution %}
Possible types
int
Default value
-1 (the number of threads is equal to the number of processor cores)
verbose
Description
Output the measured evaluation metric to stderr.
Possible types
bool
Default value
None
Return value
Generator that produces predictions with a sequentially growing subset of trees from the model. The type of generated values depends on the number of input objects:
- Single object — One-dimensional numpy.ndarray with probabilities for every class.
- Multiple objects — Two-dimensional numpy.ndarray of shape
(number_of_objects, number_of_classes)with the probability for every class for each object.