predict_proba

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

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:

Method call format

predict_proba(X,
              ntree_start=0,
              ntree_end=0,
              thread_count=-1,
              verbose=None)

Parameters

X

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)

thread_count

Description

The number of threads to use.

Optimizes the speed of execution. This parameter doesn't affect results.

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

Predictions for the given dataset.

The return value type 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.
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