---
metadata:
- name: generator
content: Diplodoc Platform v5.50.3
alternate:
- https://catboost.ai/docs/en/concepts/algorithm-missing-values-processing.md
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt
# Missing values processing
The missing values processing mode depends on the feature type and the selected package.
## Numerical features {#numerical-features}
CatBoost interprets the value of a numerical feature as a missing value if it is equal to one of the following values, which are package-dependant:
{% list tabs %}
- Python package
- `None`
- [Floating point NaN value](https://en.wikipedia.org/wiki/NaN)
- One of the following strings when loading the values from files or as Python strings:
, #N/A
, #N/A N/A
, #NA
, -1.#IND
, -1.#QNAN
, -NaN
, -nan
, 1.#IND
, 1.#QNAN
, N/A
, NA
, NULL
, NaN
, n/a
, nan
, null
, NAN
, Na
, na
, Null
, none
, None
, -
This is an extended version of the default missing values list in [pandas](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html).
- R package
- [Floating point NaN value](https://en.wikipedia.org/wiki/NaN)
- One of the following strings when loading the values from files:
, #N/A
, #N/A N/A
, #NA
, -1.#IND
, -1.#QNAN
, -NaN
, -nan
, 1.#IND
, 1.#QNAN
, N/A
, NA
, NULL
, NaN
, n/a
, nan
, null
, NAN
, Na
, na
, Null
, none
, None
, -
This is an extended version of the default missing values list in [pandas](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html).
- Command-line version
One of the following strings when loading the values from files when reading from an input file:
, #N/A
, #N/A N/A
, #NA
, -1.#IND
, -1.#QNAN
, -NaN
, -nan
, 1.#IND
, 1.#QNAN
, N/A
, NA
, NULL
, NaN
, n/a
, nan
, null
, NAN
, Na
, na
, Null
, none
, None
, -
This is an extended version of the default missing values list in [pandas](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html).
{% endlist %}
The following modes for processing missing values are supported:
- "Forbidden" — Missing values are not supported, their presence is interpreted as an error.
- "Min" — Missing values are processed as the minimum value (less than all other values) for the feature. It is guaranteed that a split that separates missing values from all other values is considered when selecting trees.
- "Max" — Missing values are processed as the maximum value (greater than all other values) for the feature. It is guaranteed that a split that separates missing values from all other values is considered when selecting trees.
The default processing mode is Min. The methods for changing the default mode are package-dependant:
{% list tabs %}
- Python package
- Globally for all features in the `nan_mode` [training parameter](https://catboost.ai/docs/en/references/training-parameters/index.md).
- Individually for each feature in the [Custom quantization borders and missing value modes](https://catboost.ai/docs/en/concepts/input-data_custom-borders.md) input file. Such values override the global default setting.
- R package
- Globally for all features in the `nan_mode` [training parameter](https://catboost.ai/docs/en/references/training-parameters/index.md).
- Individually for each feature in the [Custom quantization borders and missing value modes](https://catboost.ai/docs/en/concepts/input-data_custom-borders.md) input file. Such values override the global default setting.
- Command-line version
- Globally for all features in the `--nan-mode` [training parameter](https://catboost.ai/docs/en/references/training-parameters/index.md).
- Individually for each feature in the [Custom quantization borders and missing value modes](https://catboost.ai/docs/en/concepts/input-data_custom-borders.md) input file. Such values override the global default setting.
{% endlist %}
## Categorical features {#categorical-features}
CatBoost does not process categorical features in any specific way.