CatBoost
- Purpose
- Parameters
- Attributes
- Methods
- fit
- predict
- calc_feature_statistics
- calc_leaf_indexes
- compare
- copy
- eval_metrics
- get_all_params
- get_best_iteration
- get_best_score
- get_borders
- get_evals_result
- get_feature_importance
- get_metadata
- get_object_importance
- get_param
- get_params
- get_scale_and_bias
- get_test_eval
- grid_search
- load_model
- plot_predictions
- plot_tree
- randomized_search
- save_borders
- select_features
- set_feature_names
- set_params
- set_scale_and_bias
- shrink
- staged_predict
class CatBoost(params=None)
Purpose
Training and applying models.
Parameters
params
Description
The list of parameters to start training with.
If omitted, default values are used.
Note
Possible types: dict
Default value
None
Attributes
tree_count_
Return the number of trees in the model.
This number can differ from the value specified in the --iterations
training parameter in the following cases:
- The training is stopped by the overfitting detector.
- The
--use-best-model
training parameter is set toTrue
.
feature_importances_
Return the calculated feature importances. The output data depends on the type of the model's loss function:
- Non-ranking loss functions — PredictionValuesChange
- Ranking loss functions — LossFunctionChange
random_seed_
The random seed used for training.
learning_rate_
The learning rate used for training.
feature_names_
The names of features in the dataset.
evals_result_
Return the values of metrics calculated during the training.
best_score_
Return the best result for each metric calculated on each validation dataset.
best_iteration_
Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.
classes_
Return the names of classes for classification models. An empty list is returned for all other models.
The order of classes in this list corresponds to the order of classes in resulting predictions.
Methods
fit
Train a model.
predict
Apply the model to the given dataset.
calc_feature_statistics
Calculate and plot a set of statistics for the chosen feature.
calc_leaf_indexes
Returns indexes of leafs to which objects from pool are mapped by model trees.
compare
Draw train and evaluation metrics in Jupyter Notebook for two trained models.
copy
Copy the CatBoost object.
eval_metrics
Calculate the specified metrics for the specified dataset.
get_all_params
Return the values of all training parameters (including the ones that are not explicitly specified by users).
get_best_iteration
Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.
get_best_score
Return the best result for each metric calculated on each validation dataset.
get_borders
Return the list of borders for numerical features.
get_evals_result
Return the values of metrics calculated during the training.
get_feature_importance
Calculate and return the feature importances.
get_metadata
Return a proxy object with metadata from the model's internal key-value string storage.
get_object_importance
Calculate the effect of objects from the train dataset on the optimized metric values for the objects from the input dataset:
- Positive values reflect that the optimized metric increases.
- Negative values reflect that the optimized metric decreases.
get_param
Return the value of the given parameter if it is explicitly by the user before starting the training. If this parameter is used with the default value, this function returns None.
get_params
Return the values of training parameters that are explicitly specified by the user. If all parameters are used with their default values, this function returns an empty dict.
get_scale_and_bias
Return the scale and bias of the model.
These values affect the results of applying the model, since the model prediction results are calculated as follows:
get_test_eval
Return the formula values that were calculated for the objects from the validation dataset provided for training.
grid_search
A simple grid search over specified parameter values for a model.
load_model
Load the model from a file.
plot_predictions
Sequentially vary the value of the specified features to put them into all buckets and calculate predictions for the input objects accordingly.
plot_tree
Visualize the CatBoost decision trees.
randomized_search
A simple randomized search on hyperparameters.
save_borders
Save the model borders to a file.
select_features
Select the best features from the dataset using the Recursive Feature Elimination algorithm.
set_feature_names
Set names for all features in the model.
set_params
Set the training parameters.
set_scale_and_bias
Set the scale and bias.
shrink
Shrink the model. Only trees with indices from the range [ntree_start, ntree_end)
are kept.
staged_predict
Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range [0; i).