R package training parameters

Note.

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

Parameter Description Default value Supported processing units
Common parameters
loss_function

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

CPU and GPU

custom_loss

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
  • NumErrors
  • MCC
  • BrierScore
  • HingeLoss
  • HammingLoss
  • ZeroOneLoss
  • MSLE
  • MedianAbsoluteError
  • Huber
  • PairAccuracy
  • AverageGain
  • PFound
  • NDCG
  • PrecisionAt
  • RecallAt
  • MAP
  • CtrFactor
Examples:
  • Calculate the value of CrossEntropy:

    c('CrossEntropy')
    Or simply:
    'CrossEntropy'
  • Calculate the values of Logloss and AUC:

    c('Logloss', 'AUC')
  • Calculate the value of Quantile with the coefficient 
    c('Quantile:alpha=0.1')

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.

None

CPU and GPU

eval_metric

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
  • NumErrors
  • MCC
  • BrierScore
  • HingeLoss
  • HammingLoss
  • ZeroOneLoss
  • MSLE
  • MedianAbsoluteError
  • Huber
  • PairAccuracy
  • AverageGain
  • PFound
  • NDCG
  • PrecisionAt
  • RecallAt
  • MAP
Quantile:alpha=0.3
Optimized objective is used

CPU and GPU

iterations

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

The learning rate.

Used for reducing the gradient step.

The default value is defined automatically for binary classification based on the dataset properties and the number of iterations if none of these parameters is set. In this case, the selected learning rate is printed to stdout and saved in the model.

In other cases, the default value is 0.03.

CPU and GPU

random_seed

The random seed used for training.

0

CPU and GPU

l2_leaf_reg