What is the signifies of eta and nthread in xgboost model?



I am currently solving a problem of classification using xgboost algorithm in R .First I have read the attributes which are needed for creation of a classification model .

model <- xgboost(data, label , max.depth , eta , nthread , nround , objective)

data-Input data

label- target variable

max.depth-the depth of tree.

nround-the number of trees to the model.

objective -for regression use ‘reg:linear’ and for binary classification use ‘binary:logistic’.

I am not able to understand what should be the value of eta and nthread and how their value will effect the classification model.


eta- step size shrinkage used in an update to prevents overfitting.After each boosting step, we can directly get the weights of new features and eta actually shrinks the feature weights the boosting process more conservative.
value ranges between 0 and 1.
Default value is 0.3

nthread-the number of CPU threads we are going to use.
there is no default value of it.

Hope this helps!