predict

Apply the model to the given dataset.

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(data, 
        prediction_type='Class', 
        ntree_start=0, 
        ntree_end=0, 
        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

A file or matrix with the input dataset.

Required parameter
prediction_type string

The required prediction type.

Supported prediction types:
  • Probability
  • Class
  • RawFormulaVal
Class
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 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

A file or matrix with the input dataset.

Required parameter
prediction_type string

The required prediction type.

Supported prediction types:
  • Probability
  • Class
  • RawFormulaVal
Class
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 processor cores)
verbose bool

Output the measured evaluation metric to stderr.

None

Type of return value

numpy.array. The type of array elements depends on the specified value of the prediction_type parameter:
  • Probability or RawFormulaVal — Floating point numbers
  • Class — A number that represents the class or a string that represents the label of the class (depends on the specification of the classes in the Training dataset).