--- metadata: - name: generator content: Diplodoc Platform v5.52.0 alternate: - https://catboost.ai/docs/en/concepts/python-reference_catboostranker_predict.md - href: en/concepts/python-reference_catboostranker_predict.md type: text/markdown title: Markdown version - href: ../llms.txt type: text/markdown title: llms.txt --- > **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt # predict Apply the model to the given dataset. {% note info %} 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](https://catboost.ai/docs/en/concepts/python-features-data__desc.md) - [catboost.Pool](https://catboost.ai/docs/en/concepts/python-reference_pool.md) - [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html) (in this case, feature names are taken from column names) - [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) (in this case, feature names are taken from column names) {% endnote %} ## Method call format {#call-format} ```python predict(data, prediction_type=None, ntree_start=0, ntree_end=0, thread_count=-1 (the number of threads is equal to the number of processor cores), verbose=None, task_type="CPU") ``` ## Parameters {#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: - scipy.sparse.spmatrix (all subclasses except dia_matrix) - catboost.Pool - list of lists - numpy.ndarray of shape `(object_count, feature_count)` - pandas.DataFrame - pandas.SparseDataFrame - pandas.Series - [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) - [catboost.FeaturesData](https://catboost.ai/docs/en/concepts/python-features-data__desc.md) 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** None (Exponent for Poisson and Tweedie, RawFormulaVal for all other loss functions) ### 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)`. 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)`. 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 (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 calculate prediction. 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 ### 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 {#output-format} 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.