---
metadata:
- name: generator
content: Diplodoc Platform v5.50.3
alternate:
- https://catboost.ai/docs/en/concepts/python-reference_catboost_select_features.md
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt
# select_features
## Purpose {#purpose}
Select the best features and drop harmful features from the dataset.
## Method call format {#call-format}
```python
select_features(X,
y=None,
eval_set=None,
features_for_select=None,
num_features_to_select=None,
algorithm=None,
steps=None,
shap_calc_type=None,
train_final_model=False,
verbose=None,
logging_level=None,
plot=False,
log_cout=sys.stdout,
log_cerr=sys.stderr)
```
## Parameters {#parameters}
### X
#### Description
The input training dataset.
{% note info %}
If a nontrivial value of the `cat_features` parameter is specified in the constructor of this class, CatBoost checks the equivalence of categorical features indices specification from the constructor parameters and in this Pool class.
{% endnote %}
**Possible types**
catboost.Pool
**Default value**
Required parameter
**Supported processing units**
CPU and GPU
### y
#### Description
The target variables (in other words, the objects' label values) for the training dataset.
Must be in the form of a one- or two- dimensional array. The type of data in the array depends on the machine learning task being solved:
- Regression and ranking — One-dimensional array of numeric values.
- Multiregression - Two-dimensional array of numeric values. The first index is for a dimension, the second index is for an object.
- Binary classification
One-dimensional array containing one of:
* Booleans, integers or strings that represent the labels of the classes (only two unique values).
* Numeric values.
The interpretation of numeric values depends on the selected loss function:
- Logloss — The value is considered a positive class if it is strictly greater than the value of the `target_border` training parameter. Otherwise, it is considered a negative class.
- CrossEntropy — The value is interpreted as the probability that the dataset object belongs to the positive class. Possible values are in the range `[0; 1]`.
- Multiclassification — One-dimensional array of integers or strings that represent the labels of the classes.
- Multi label classification
Two-dimensional array. The first index is for a label/class, the second index is for an object.
Possible values depend on the selected loss function:
* MultiLogloss — Only {0, 1} or {False, True} values are allowed that specify whether an object belongs to the class corresponding to the first index.
* MultiCrossEntropy — Numerical values in the range `[0; 1]` that are interpreted as the probability that the dataset object belongs to the class corresponding to the first index.
{% note info %}
Do not use this parameter if the input training dataset (specified in the `X` parameter) type is catboost.Pool.
{% endnote %}
**Possible types**
- list
- numpy.ndarray
- pandas.DataFrame
- pandas.Series
- [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html)
- [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html)
**Default value**
None
**Supported processing units**
CPU and GPU
### eval_set
#### Description
The validation dataset or datasets used for the following processes:
- [overfitting detector](https://catboost.ai/docs/en/concepts/overfitting-detector.md)
- the best iteration selection
- monitoring metrics' changes
**Possible types**
- catboost.Pool
- tuple (X, y)
- string (path to the dataset file)
**Default value**
None
**Supported processing units**
CPU and GPU
### features_for_select
#### Description
Features which participate in the selection. The following formats are supported:
- A list with indices, names, index ranges, name ranges. For example: `[0, 3, 5, 6, '10-15', 'City', 'Player1-Player11']`.
- A string with indices, names, index ranges, name ranges. Values are separated by commas, for example: `0,3,5,6,10-15,City,Player1-Player11`.
**Possible types**
- list
- string
**Default value**
Required parameter
**Supported processing units**
CPU and GPU
### num_features_to_select
#### Description
The number of features to select from `features_for_select`.
**Possible types**
int
**Default value**
Required parameter
**Supported processing units**
CPU and GPU
### steps
#### Description
The number of times for training the model. Use more steps for more accurate selection.
**Possible types**
int
**Default value**
1
**Supported processing units**
CPU and GPU
### algorithm
#### Description
The main algorithm is [Recursive Feature Elimination](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html) with variable feature importance calculation method:
- `RecursiveByPredictionValuesChange` — the fastest algorithm and the least accurate method (not recommended for ranking losses).
- `RecursiveByLossFunctionChange` — the optimal option according to accuracy/speed balance.
- `RecursiveByShapValues` — the most accurate method.
**Possible types**
EFeaturesSelectionAlgorithm
**Default value**
RecursiveByShapValues
**Supported processing units**
CPU and GPU
### shap_calc_type
#### Description
The method of the SHAP values calculations ordered by accuracy:
- `Approximate`
- `Regular`
- `Exact`
Used in RecursiveByLossFunctionChange and RecursiveByShapValues.
**Possible types**
EShapCalcType
**Default value**
Regular
**Supported processing units**
CPU and GPU
### train_final_model
#### Description
If specified, then the model with selected features will be trained after features selection.
**Possible types**
bool
**Default value**
True
**Supported processing units**
CPU and GPU
### verbose
_Alias:_`verbose_eval`
#### Description
The purpose of this parameter depends on the type of the given value:
- bool — Defines the logging level:
- True
corresponds to the Verbose logging level
- False
corresponds to the Silent logging level
- int — Use the Verbose logging level and set the logging period to the value of this parameter.
{% note alert %}
Do not use this parameter with the `logging_level` parameter.
{% endnote %}
**Possible types**
- bool
- int
**Default value**
1
**Supported processing units**
CPU and GPU
### logging_level
#### Description
The logging level to output to stdout.
Possible values:
- Silent — Do not output any logging information to stdout.
- Verbose — Output the following data to stdout:
- optimized metric
- elapsed time of training
- remaining time of training
- Info — Output additional information and the number of trees.
- Debug — Output debugging information.
**Possible types**
string
**Default value**
None (corresponds to the Verbose logging level)
**Supported processing units**
CPU and GPU
### plot
#### Description
Plot the following information during training:
- the metric values;
- the custom loss values;
- the loss function change during feature selection;
- the time has passed since training started;
- the remaining time until the end of training.
This [option can be used](https://catboost.ai/docs/en/features/visualization_jupyter-notebook.md) if training is performed in Jupyter notebook.
**Possible types**
bool
**Default value**
False
**Supported processing units**
CPU
### log_cout
Output stream or callback for logging.
**Possible types**
- callable Python object
- python object providing the `write()` method
**Default value**
sys.stdout
### log_cerr
Error stream or callback for logging.
**Possible types**
- callable Python object
- python object providing the `write()` method
**Default value**
sys.stderr
## Return value {#output-format}
Dict with four fields:
- `selected_features` — a list with indices of selected features.
- `selected_features_names` — a list with names of selected features, if feature names were specified.
- `eliminated_features` — a list with indices of eliminated features.
- `eliminated_features_names` — a list with names of eliminated features, if feature names were specified.
## Examples {#example}
```python
from catboost import CatBoost, Pool, EShapCalcType, EFeaturesSelectionAlgorithm
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
X, y = make_regression(n_samples=1000, n_features=100, n_informative=20, random_state=0)
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.25, random_state=0)
feature_names = ['F{}'.format(i) for i in range(train_X.shape[1])]
train_pool = Pool(train_X, train_y, feature_names=feature_names)
test_pool = Pool(test_X, test_y, feature_names=feature_names)
model = CatBoost(loss_function='RMSE', iterations=1000, random_seed=0)
summary = model.select_features(
train_pool,
eval_set=test_pool,
features_for_select='0-99',
num_features_to_select=10,
steps=3,
algorithm=EFeaturesSelectionAlgorithm.RecursiveByShapValues,
shap_calc_type=EShapCalcType.Regular,
train_final_model=True,
logging_level='Silent',
plot=True
)
```
The following is a chart plotted with [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook.md) for the given example.
