Create setting for gradient boosting machine model using lightGBM (https://github.com/microsoft/LightGBM/tree/master/R-package).

Create setting for gradient boosting machine model using lightGBM (https://github.com/microsoft/LightGBM/tree/master/R-package).

setLightGBM(
  nthread = 20,
  earlyStopRound = 25,
  numIterations = c(100),
  numLeaves = c(31),
  maxDepth = c(5, 10),
  minDataInLeaf = c(20),
  learningRate = c(0.05, 0.1, 0.3),
  lambdaL1 = c(0),
  lambdaL2 = c(0),
  scalePosWeight = 1,
  isUnbalance = FALSE,
  seed = sample(1e+07, 1)
)

Arguments

nthread

The number of computer threads to use (how many cores do you have?)

earlyStopRound

If the performance does not increase over earlyStopRound number of trees then training stops (this prevents overfitting)

numIterations

Number of boosting iterations.

numLeaves

This hyperparameter sets the maximum number of leaves. Increasing this parameter can lead to higher model complexity and potential overfitting.

maxDepth

This hyperparameter sets the maximum depth . Increasing this parameter can also lead to higher model complexity and potential overfitting.

minDataInLeaf

This hyperparameter sets the minimum number of data points that must be present in a leaf node. Increasing this parameter can help to reduce overfitting

learningRate

This hyperparameter controls the step size at each iteration of the gradient descent algorithm. Lower values can lead to slower convergence but may result in better performance.

lambdaL1

This hyperparameter controls L1 regularization, which can help to reduce overfitting by encouraging sparse models.

lambdaL2

This hyperparameter controls L2 regularization, which can also help to reduce overfitting by discouraging large weights in the model.

scalePosWeight

Controls weight of positive class in loss - useful for imbalanced classes

isUnbalance

This parameter cannot be used at the same time with scalePosWeight, choose only one of them. While enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities.

seed

An option to add a seed when training the final model

Examples

model.lightgbm <- setLightGBM(
    numLeaves = c(20, 31, 50), maxDepth = c(-1, 5, 10),
    minDataInLeaf = c(10, 20, 30), learningRate = c(0.05, 0.1, 0.3)
)