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 $\text{[math]}$ : `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 $\text{[math]}$ `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