predict

Apply the model to the given dataset.

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(data,
        prediction_type='Class',
        ntree_start=0,
        ntree_end=0,
        thread_count=-1,
        verbose=None,
        task_type="CPU")

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

prediction_type

Description

The required prediction type.

  • Supported prediction types:
  • Probability
  • Class
  • RawFormulaVal
  • Exponent
  • LogProbability

Possible types

string

Default value

Class

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.
{% 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

task_type

Description

The evaluator type.

Possible values:
- 'CPU'
- 'GPU' (models with only numerical features are supported for now)

Possible types

string

Default value

CPU

Return value

Predictions for the given dataset.

The return value type depends on the number of input objects:

  • Single object — The returned value depends on the specified value of the prediction_type parameter:

    • RawFormulaVal — Raw formula value.

    • Class — Class label.

    • Probability — One-dimensional numpy.ndarray with the probability for every class.

  • Multiple objects — The returned value depends on the specified value of the prediction_type parameter:

    • RawFormulaVal — One-dimensional numpy.ndarray of raw formula values (one for each object).

    • Class — One-dimensional numpy.ndarray of class label (one for each object).

    • Probability — 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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