Training parameters

Several parameters have aliases. For example, the iterations parameter has the following synonyms: num_boost_round, n_estimators, num_trees. Simultaneous usage of different names of one parameter raises an error.

Training on GPU requires NVIDIA Driver of version 390.xx or higher.

ParameterTypeDescriptionDefault valueSupported processing units
Common parameters

loss_function

Alias: objective

  • string
  • object

The metric to use in training. The specified value also determines the machine learning problem to solve. Some metrics support optional parameters (see the Objectives and metrics section for details on each metric).

Format:
<Metric>[:<parameter 1>=<value>;..;<parameter N>=<value>]
Supported metrics:
  • RMSE
  • Logloss
  • MAE
  • CrossEntropy
  • Quantile
  • LogLinQuantile
  • Lq
  • MultiClass
  • MultiClassOneVsAll
  • MAPE
  • Poisson
  • PairLogit
  • PairLogitPairwise
  • QueryRMSE
  • QuerySoftMax
  • YetiRank
  • YetiRankPairwise

A custom python object can also be set as the value of this parameter (see an example).

For example, use the following construction to calculate the value of Quantile with the coefficient :
Quantile:alpha=0.1

RMSE

CPU and GPU

custom_metric
  • string
  • list of strings

Metric values to output during training. These functions are not optimized and are displayed for informational purposes only. Some metrics support optional parameters (see the Objectives and metrics section for details on each metric)..

Format:
<Metric>[:<parameter 1>=<value>;..;<parameter N>=<value>]
Supported metrics:
  • RMSE
  • Logloss
  • MAE
  • CrossEntropy
  • Quantile
  • LogLinQuantile
  • Lq
  • MultiClass
  • MultiClassOneVsAll
  • MAPE
  • Poisson
  • PairLogit
  • PairLogitPairwise
  • QueryRMSE
  • QuerySoftMax
  • SMAPE
  • Recall
  • Precision
  • F1
  • TotalF1
  • Accuracy
  • BalancedAccuracy
  • BalancedErrorRate
  • Kappa
  • WKappa
  • LogLikelihoodOfPrediction
  • AUC
  • R2
  • MCC
  • BrierScore
  • HingeLoss
  • HammingLoss
  • ZeroOneLoss
  • MSLE
  • MedianAbsoluteError
  • PairAccuracy
  • AverageGain
  • PFound
  • NDCG
  • PrecisionAt
  • RecallAt
  • MAP
  • CtrFactor
Examples:
  • Calculate the value of CrossEntropy:

    CrossEntropy
  • Calculate the value of в with the coefficient 
    Quantile:alpha=0.1
  • Calculate the values of Logloss and AUC:
    ['Logloss', 'AUC']

Values of all custom metrics for learn and validation datasets are saved to the Metric output files (learn_error.tsv and test_error.tsv respectively). The directory for these files is specified in the --train-dir (train_dir) parameter.

Use the visualization tools to see a live chart with the dynamics of the specified metrics.

None (use one of the metrics supported by the library)

CPU

eval_metric
  • string
  • object

The metric used for overfitting detection (if enabled) and best model selection (if enabled). Some metrics support optional parameters (see the Objectives and metrics section for details on each metric).

Format:
<Metric>[:<parameter 1>=<value>;..;<parameter N>=<value>]
Supported metrics:
  • RMSE
  • Logloss
  • MAE
  • CrossEntropy
  • Quantile
  • LogLinQuantile
  • Lq
  • MultiClass
  • MultiClassOneVsAll
  • MAPE
  • Poisson
  • PairLogit
  • PairLogitPairwise
  • QueryRMSE
  • QuerySoftMax
  • SMAPE
  • Recall
  • Precision
  • F1
  • TotalF1
  • Accuracy
  • BalancedAccuracy
  • BalancedErrorRate
  • Kappa
  • WKappa
  • LogLikelihoodOfPrediction
  • AUC
  • R2
  • MCC
  • BrierScore
  • HingeLoss
  • HammingLoss
  • ZeroOneLoss
  • MSLE
  • MedianAbsoluteError
  • PairAccuracy
  • AverageGain
  • PFound
  • NDCG
  • PrecisionAt
  • RecallAt
  • MAP

A user-defined function can also be set as the value (see an example).

Examples:
R2
Optimized objective is used

CPU

iterations

Aliases:
  • num_boost_round
  • n_estimators
  • num_trees
int

The maximum number of trees that can be built when solving machine learning problems.

When using other parameters that limit the number of iterations, the final number of trees may be less than the number specified in this parameter.

1000

CPU and GPU

learning_rate

Alias: eta

float

The learning rate.

Used for reducing the gradient step.

The default value is defined automatically based on the dataset properties and training parameters if all of the following conditions are met:

  • The binary classification machine learning problem is being solved.

  • Some parameters are not set (refer to the list)

The value is set to 0.03 otherwise.

CPU and GPU

random_seed

Alias: random_state

int

The random seed used for training.

None (0)

CPU and GPU

l2_leaf_reg

Alias: reg_lambda

int

L2 regularization coefficient. Used for leaf value calculation.

Any positive values are allowed.

3

CPU and GPU

bootstrap_typestring

Bootstrap type. Defines the method for sampling the weights of objects.

Supported methods:

  • Poisson (supported for GPU only)
  • Bayesian
  • Bernoulli
  • No
Bayesian

CPU and GPU

bagging_temperaturefloat

Defines the settings of the Bayesian bootstrap. It is used by default in classification and regression modes.

Use the Bayesian bootstrap to assign random weights to objects.

The weights are sampled from exponential distribution if the value of this parameter is set to “1”. All weights are equal to 1 if the value of this parameter is set to “0”.

Possible values are in the range . The higher the value the more aggressive the bagging is.

1

CPU and GPU

subsamplefloat
Sample rate for bagging. This parameter can be used if one of the following bootstrap types is defined:
  • Poisson
  • Bernoulli
0.66

CPU and GPU

sampling_frequencystring

Frequency to sample weights and objects when building trees.

Supported values:
  • PerTree
  • PerTreeLevel
PerTreeLevel

CPU and GPU

random_strengthfloat

Score the standard deviation multiplier. Use this parameter to avoid overfitting the model.

The value of this parameter is used when selecting splits. On every iteration each possible split gets a score (for example, the score indicates how much adding this split will improve the loss function for the training dataset). The split with the highest score is selected.

The scores have no randomness. A normally distributed random variable is added to the score of the feature. It has a zero mean and a variance that decreases during the training. The value of this parameter is the multiplier of the variance.

1

CPU and GPU

use_best_modelbool
If this parameter is set, the number of trees that are saved in the resulting model is defined as follows:
  1. Build the number of trees defined by the training parameters.
  2. Use the validation dataset to identify the iteration with the optimal value of the metric specified in  --eval-metric (eval_metric).

No trees are saved after this iteration.

This option requires a validation dataset to be provided.

True if a validation set is input (the eval_set parameter is defined) and at least one of the label values of objects in this set differs from the others. False otherwise.

CPU and GPU

best_model_min_treesint

The minimal number of trees that the best model should have. If set, the output model contains at least the given number of trees even if the best model is located within these trees.

Should be used with the use_best_model parameter.

None (The minimal number of trees for the best model is not set)

CPU and GPU

depth

Alias: max_depth

int

Depth of the tree.

The range of supported values depends on the processing unit type and the type of the selected loss function:
  • CPU — Any integer up to  16.

  • GPU — Any integer up to 8 pairwise modes (YetiRank, PairLogitPairwise and QueryCrossEntropy) and up to   16 for all other loss functions.

6

CPU and GPU

ignored_featureslist

Indices of features to exclude from training. The non-negative indices that do not match any features are successfully ignored. For example, if five features are defined for the objects in the dataset and this parameter is set to “42”, the corresponding non-existing feature is successfully ignored.

The identifier corresponds to the feature's index. Feature indices used in train and feature importance are numbered from 0 to featureCount – 1. If a file is used as input data then any non-feature column types are ignored when calculating these indices. For example, each row in the input file contains data in the following order: categorical feature<\t>label value<\t>numerical feature. So for the row rock<\t>0<\t>42, the identifier for the “rock” feature is 0, and for the “42” feature it's 1.

Supported operators:

  • “:” — Value separator.
  • “-” — Range of values (the left and right edges are included).
For example, if training should exclude features with the identifiers 1, 2, 7, 42, 43, 44, 45, use the following construction:
1:2:7:42-45
None (use all features)

CPU and GPU

one_hot_max_sizeint

Use one-hot encoding for all features with a number of different values less than or equal to the given parameter value. Ctrs are not calculated for such features.

2

CPU and GPU

has_timebool

Use the order of objects in the input data (do not perform random permutations during the Transforming categorical features to numerical features and Choosing the tree structure stages).

The Timestamp column type is used to determine the order of objects if specified in the input data.

False (not used; generates random permutations)

CPU and GPU

rsm

Alias: colsample_bylevel

float (0;1]

Random subspace method. The percentage of features to use at each split selection, when features are selected over again at random.

The value must be in the range (0;1].

None (set to 1)

CPU

nan_modestring

The method to process NaN values in the input dataset.

Possible values:
  • Forbidden — NaN values are not supported, their presence raises an exception.
  • Min — Each NaN float feature is processed as the minimum value from the dataset.
  • Max — Each NaN float feature is processed as the maximum value from the dataset.
Note.

The method for processing NaN values can also be set in the Custom quantization borders and NaN modes input file. Such values override the ones specified in this parameter.

Min

CPU and GPU

fold_permutation_block_sizeint

Objects in the dataset are grouped in blocks before the random permutations. This parameter defines the size of the blocks. The smaller is the value, the slower is the training. Large values may result in quality degradation.

1

CPU and GPU

leaf_estimation_iterationsint

The number of gradient steps when calculating the values in leaves.

None (Depends on the training objective)

CPU and GPU

leaf_estimation_methodstring

The method used to calculate the values in leaves.

Possible values:
  • Newton
  • Gradient
Gradient

CPU and GPU

fold_len_multiplierfloat

Coefficient for changing the length of folds.

The value must be greater than 1. The best validation result is achieved with minimum values.

With values close to 1 (for example, ), each iteration takes a quadratic amount of memory and time for the number of objects in the iteration. Thus, low values are possible only when there is a small number of objects.

2

CPU and GPU

approx_on_full_historybool

The principles for calculating the approximated values.

Possible values:
  • “False” — Use only а fraction of the fold for calculating the approximated values. The size of the fraction is calculated as follows: , where X is the specified coefficient for changing the length of folds. This mode is faster and in rare cases slightly less accurate
  • “True” — Use all the preceding rows in the fold for calculating the approximated values. This mode is slower and in rare cases slightly more accurate.
False

CPU

class_weightslist

Class weights. The values are used as multipliers for the object weights. This parameter can be used for solving classification and multiclassification problems.

Tip.

For imbalanced datasets with binary classification the weight multiplier can be set to 1 for class 0 and to for class 1.

For example, class_weights=[0.1, 4] multiplies the weights of objects from class 0 by 0.1 and the weights of objects from class 1 by 4.

None (the weight for all classes is set to 1)

CPU and GPU

scale_pos_weight (alias for: class_weights)float

The weight for class 1 in binary classification. The value is used as a multiplier for the weights of objects from class 1.

Tip. For imbalanced datasets, the weight multiplier can be set to 
1.0

CPU and GPU

boosting_typestring

Boosting scheme.

Possible values:
  • Ordered — Usually provides better quality on small datasets, but it may be slower than the Plain scheme.
  • Plain — The classic gradient boosting scheme.
Depends on the number of objects in the training dataset and the selected learning mode

CPU and GPU

Only the Plain mode is supported for the MultiClass loss on GPU

allow_const_labelbool

Use it to train models with datasets that have equal label values for all objects.

False

CPU and GPU

Overfitting detection settings
od_typestring

The type of the overfitting detector to use.

Possible values:
  • IncToDec
  • Iter
IncToDec

CPU and GPU

od_pvalfloat

The threshold for the IncToDec overfitting detector type. The training is stopped when the specified value is reached. Requires that a validation dataset was input.

For best results, it is recommended to set a value in the range .

The larger the value, the earlier overfitting is detected.

Restriction.

Do not use this parameter with the Iter overfitting detector type.

0 (the overfitting detection is turned off)

CPU and GPU

od_waitintThe number of iterations to continue the training after the iteration with the optimal metric value.
The purpose of this parameter differs depending on the selected overfitting detector type:
  • IncToDec — Ignore the overfitting detector when the threshold is reached and continue learning for the specified number of iterations after the iteration with the optimal metric value.
  • Iter — Consider the model overfitted and stop training after the specified number of iterations since the iteration with the optimal metric value.
20

CPU and GPU

early_stopping_roundsintSet the overfitting detector type to Iter and stop the training after the specified number of iterations since the iteration with the optimal metric value.False

CPU and GPU

Binarization settings

border_count

Alias: max_bin

int

The number of splits for numerical features. Allowed values are integers from 1 to 255 inclusively.

254 (if training is performed on CPU) or 128 (if training is performed on GPU)

CPU and GPU

feature_border_typestring

The binarization mode for numerical features.

Possible values:
  • Median
  • Uniform
  • UniformAndQuantiles
  • MaxLogSum
  • MinEntropy
  • GreedyLogSum
GreedyLogSum

CPU and GPU

Multiclassification settings
classes_countint

The upper limit for the numeric class label. Defines the number of classes for multiclassification.

Only non-negative integers can be specified. The given integer should be greater than any of the label values.

If this parameter is specified the labels for all classes in the input dataset should be smaller than the given value

None.

Calculation principles

CPU and GPU

Performance settings
thread_countint

The number of threads to use during training.

The purpose depends on the selected processing unit:
  • CPU

    • For CPU

      Optimizes the speed of execution. This parameter doesn't affect results.

    • For GPU

      The given value is used for reading the data from the hard drive and does not affect the training.

      During the training one main thread and one thread for each GPU are used.

  • GPU

    The given value is used for reading the data from the hard drive and does not affect the training.

    During the training one main thread and one thread for each GPU are used.

-1 (the number of threads is equal to the number of cores)The number of processor cores)

CPU and GPU

used_ram_limitint

Attempt to limit the amount of used CPU RAM.

Restriction.
  • This option affects only the CTR calculation memory usage.
  • In some cases it is impossible to limit the amount of CPU RAM used in accordance with the specified value.
Format:
<size><measure of information>
Supported measures of information (non case-sensitive):
  • MB
  • KB
  • GB
For example:
2gb
None (memory usage is no limited)

CPU

gpu_ram_partfloat

How much of the GPU RAM to use for training.

0.95

GPU

pinned_memory_sizeint

How much pinned (page-locked) CPU RAM to use per GPU.

1073741824

GPU

gpu_cat_features_storagestring

The method for storing the categorical features' values.

Possible values:
  • CpuPinnedMemory
  • GpuRam
Tip.

Use the CpuPinnedMemory value if feature combinations are used and the available GPU RAM is not sufficient.

None (set to GpuRam)

GPU

data_partitionstring

The method for splitting the input dataset between multiple workers.

Possible values:
  • FeatureParallel — Split the input dataset by features and calculate the value of each of these features on a certain GPU.

    For example:

    • GPU0 is used to calculate the values of features indexed 0, 1, 2
    • GPU1 is used to calculate the values of features indexed 3, 4, 5, etc.
  • DocParallel — Split the input dataset by objects and calculate all features for each of these objects on a certain GPU. It is recommended to use powers of two as the value for optimal performance.

    For example:
    • GPU0 is used to calculate all features for objects indexed object_1, object_2
    • GPU1 is used to calculate all features for objects indexed object_3, object_4, etc.
Depends on the learning mode and the input dataset

GPU

Processing unit settings
task_typestring

The processing unit type to use for training.

Possible values:
  • CPU
  • GPU
CPU

CPU and GPU

devicesstring

IDs of the GPU devices to use for training (indices are zero-based).

Format

  • <unit ID> for one device (for example, 3)
  • <unit ID1>:<unit ID2>:..:<unit IDN> for multiple devices (for example, devices='0:1:3')
  • <unit ID1>-<unit IDN> for a range of devices (for example, devices='0-3')
NULL (all GPU devices are used if the corresponding processing unit type is selected)

GPU

Visualization settings
namestringThe experiment name to display in visualization tools.experiment

CPU and GPU

Output settings
logging_levelstring

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.
None (corresponds to the Verbose logging level)

CPU and GPU

metric_periodint

The frequency of iterations to calculate the values of objectives and metrics. The value should be a positive integer.

The usage of this parameter speeds up the training.

Note.

It is recommended to increase the value of this parameter to maintain training speed if a GPU processing unit type is used.

1

CPU and GPU

verbose

Alias: verbose_eval

  • bool
  • int

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.
Restriction. Do not use this parameter with the logging_level parameter.
1

CPU and GPU

train_dirstring

The directory for storing the files generated during training.

catboost_info

CPU and GPU

model_size_regfloat

The model size regularization coefficient. The larger the value, the smaller the model size.

Possible values are in the range .

Large values reduce the number of feature combinations in the model. Note that the resulting quality of the model can be affected. Set the value to 0 to turn off the model size optimization option.

None (corresponds to the 0.5 value)

CPU

allow_writing_filesbool

Allow to write analytical and snapshot files during training.

If set to “False”, the snapshot and data visualization tools are unavailable.

True

CPU

save_snapshotbool

Enable snapshotting for restoring the training progress after an interruption.

None

CPU and GPU

snapshot_filestringThe name of the file to save the training progress information in. This file is used for recovering training after an interruption.
Depending on whether the specified file exists in the file system:
  • Missing — Write information about training progress to the specified file.
  • Exists — Load data from the specified file and continue training from where it left off.

experiment...

CPU and GPU

snapshot_intervalint

The interval between saving snapshots in seconds.

The first snapshot is taken after the specified number of seconds since the start of training. Every subsequent snapshot is taken after the specified number of seconds since the previous one. The last snapshot is taken at the end of the training.

600

CPU and GPU

roc_filestring

The name of the output file to save the ROC curve points to. This parameter can only be set in cross-validation mode if the Logloss loss function is selected. The ROC curve points are calculated for the test fold.

The output file is saved to the catboost_info directory.

None (the file is not saved)

CPU and GPU

CTR settings
simple_ctrstring

Binarization settings for simple categorical features.

['CtrType[:TargetBorderCount=BorderCount][:TargetBorderType=BorderType][:CtrBorderCount=Count][:CtrBorderType=Type][:Prior=num_1/denum_1]..[:Prior=num_N/denum_N]',
 'CtrType[:TargetBorderCount=BorderCount][:TargetBorderType=BorderType][:CtrBorderCount=Count][:CtrBorderType=Type][:Prior=num_1/denum_1]..[:Prior=num_N/denum_N]',
  ...]
Components:
  • CtrType — The method for transforming categorical features to numerical features.

    Supported methods for training on CPU:

    • Borders
    • Buckets
    • BinarizedTargetMeanValue
    • Counter

    Supported methods for training on GPU:

    • Borders
    • Buckets
    • FeatureFreq
    • FloatTargetMeanValue
  • TargetBorderCount — The number of borders for label value binarization. Only used for regression problems. Allowed values are integers from 1 to 255 inclusively. The default value is 1.

    This option is available for training on CPU only.

  • TargetBorderType — The binarization type for the label value. Only used for regression problems.

    Possible values:

    • Median
    • Uniform
    • UniformAndQuantiles
    • MaxLogSum
    • MinEntropy
    • GreedyLogSum

    By default, MinEntropy.

    This option is available for training on CPU only.

  • CtrBorderCount — The number of splits for categorical features. Allowed values are integers from 1 to 255 inclusively.
  • CtrBorderType — The binarization type for categorical features.

    Supported values for training on CPU:
    • Uniform

    Supported values for training on GPU:

    • Median
    • Uniform
    • UniformAndQuantiles
    • MaxLogSum
    • MinEntropy
    • GreedyLogSum
  • Prior — Use the specified priors during training (several values can be specified).

    Possible formats:
    • One number — Adds the value to the numerator.
    • Two slash-delimited numbers (for GPU only) — Use this format to set a fraction. The number is added to the numerator and the second is added to the denominator.

CPU and GPU

combinations_ctrstring

Binarization settings for combinations of categorical features.

['CtrType[:TargetBorderCount=BorderCount][:TargetBorderType=BorderType][:CtrBorderCount=Count][:CtrBorderType=Type][:Prior=num_1/denum_1]..[:Prior=num_N/denum_N]',
 'CtrType[:TargetBorderCount=BorderCount][:TargetBorderType=BorderType][:CtrBorderCount=Count][:CtrBorderType=Type][:Prior=num_1/denum_1]..[:Prior=num_N/denum_N]',
  ...]
Components:
  • CtrType — The method for transforming categorical features to numerical features.

    Supported methods for training on CPU:

    • Borders
    • Buckets
    • BinarizedTargetMeanValue
    • Counter

    Supported methods for training on GPU:

    • Borders
    • Buckets
    • FeatureFreq
    • FloatTargetMeanValue
  • TargetBorderCount — The number of borders for label value binarization. Only used for regression problems. Allowed values are integers from 1 to 255 inclusively. The default value is 1.

    This option is available for training on CPU only.

  • TargetBorderType — The binarization type for the label value. Only used for regression problems.

    Possible values:

    • Median
    • Uniform
    • UniformAndQuantiles
    • MaxLogSum
    • MinEntropy
    • GreedyLogSum

    By default, MinEntropy.

    This option is available for training on CPU only.

  • CtrBorderCount — The number of splits for categorical features. Allowed values are integers from 1 to 255 inclusively.
  • CtrBorderType — The binarization type for categorical features.

    Supported values for training on CPU:
    • Uniform
    Supported values for training on GPU:
    • Uniform
    • Median
  • Prior — Use the specified priors during training (several values can be specified).

    Possible formats:
    • One number — Adds the value to the numerator.
    • Two slash-delimited numbers (for GPU only) — Use this format to set a fraction. The number is added to the numerator and the second is added to the denominator.

CPU and GPU

per_feature_ctrstring

Per-feature binarization settings for categorical features.

['FeatureId:CtrType:[:TargetBorderCount=BorderCount][:TargetBorderType=BorderType][:CtrBorderCount=Count][:CtrBorderType=Type][:Prior=num_1/denum_1]..[:Prior=num_N/denum_N]',
 'FeatureId:CtrType:[:TargetBorderCount=BorderCount][:TargetBorderType=BorderType][:CtrBorderCount=Count][:CtrBorderType=Type][:Prior=num_1/denum_1]..[:Prior=num_N/denum_N]',
  ...]
Components:
  • FeatureId — A zero-based feature identifier.
  • CtrType — The method for transforming categorical features to numerical features.

    Supported methods for training on CPU:

    • Borders
    • Buckets
    • BinarizedTargetMeanValue
    • Counter

    Supported methods for training on GPU:

    • Borders
    • Buckets
    • FeatureFreq
    • FloatTargetMeanValue
  • TargetBorderCount — The number of borders for label value binarization. Only used for regression problems. Allowed values are integers from 1 to 255 inclusively. The default value is 1.

    This option is available for training on CPU only.

  • TargetBorderType — The binarization type for the label value. Only used for regression problems.

    Possible values:

    • Median
    • Uniform
    • UniformAndQuantiles
    • MaxLogSum
    • MinEntropy
    • GreedyLogSum

    By default, MinEntropy.

    This option is available for training on CPU only.

  • CtrBorderCount — The number of splits for categorical features. Allowed values are integers from 1 to 255 inclusively.
  • CtrBorderType — The binarization type for categorical features.

    Supported values for training on CPU:
    • Uniform

    Supported values for training on GPU:

    • Median
    • Uniform
    • UniformAndQuantiles
    • MaxLogSum
    • MinEntropy
    • GreedyLogSum
  • Prior — Use the specified priors during training (several values can be specified).

    Possible formats:
    • One number — Adds the value to the numerator.
    • Two slash-delimited numbers (for GPU only) — Use this format to set a fraction. The number is added to the numerator and the second is added to the denominator.

CPU and GPU

counter_calc_methodstring

The method for calculating the Counter CTR type.

Possible values:
  • SkipTest — Objects from the validation dataset are not considered at all
  • Full — All objects from both learn and validation datasets are considered
None (Full is used)

CPU and GPU

max_ctr_complexityint

The maximum number of categorical features that can be combined.

4

CPU and GPU

ctr_leaf_count_limitint

The maximum number of leaves with categorical features. If the quantity exceeds the specified value a part of leaves is discarded.

The leaves to be discarded are selected as follows:

  1. The leaves are sorted by the frequency of the values.
  2. The top N leaves are selected, where N is the value specified in the parameter.
  3. All leaves starting from N+1 are discarded.

This option reduces the resulting model size and the amount of memory required for training. Note that the resulting quality of the model can be affected.

None

The number of leafs with categorical features is not limited

CPU

store_all_simple_ctrbool

Ignore categorical features, which are not used in feature combinations, when choosing candidates for exclusion.

Use this parameter with ctr_leaf_count_limit only.

None (set to False)

Both simple features and feature combinations are taken in account when limiting the number of leafs with categorical features

CPU

final_ctr_computation_modestring

Final CTR computation mode.

Possible values:
  • Default — Compute final CTRs for learn and validation datasets.
  • Skip — Do not compute final CTRs for learn and validation datasets. In this case, the resulting model can not be applied. This mode decreases the size of the resulting model. It can be useful for research purposes when only the metric values have to be calculated.
Default

CPU and GPU