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What is the minimum sum of instance weight in XGBoost?
Minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression task, this simply corresponds to minimum number of instances needed to be in each node.
When to use the second column in XGBoost?
Range: [0,1]. When this flag is enabled, XGBoost differentiates the importance of instances for csv input by taking the second column (the column after labels) in training data as the instance weights. When this flag is enabled, XGBoost builds histogram on GPU deterministically. Used only if tree_method is set to gpu_hist .
What are valid values in XGBoost for CSV input?
Valid values: Float. Range: [0,1]. When this flag is enabled, XGBoost differentiates the importance of instances for csv input by taking the second column (the column after labels) in training data as the instance weights. When this flag is enabled, XGBoost builds histogram on GPU deterministically.
What are the different types of XGBoost parameters?
Before running XGBoost, we must set three types of parameters: general parameters, booster parameters and task parameters. General parameters relate to which booster we are using to do boosting, commonly tree or linear model. Booster parameters depend on which booster you have chosen.
What are the parameters of XGBoost for distributed training?
XGBoost supports approx, hist and gpu_hist for distributed training. Experimental support for external memory is available for approx and gpu_hist. Choices: auto, exact, approx, hist, gpu_hist, this is a combination of commonly used updaters.
Why does XGBoost always use exact greedy algorithm?
Because old behavior is always use exact greedy in single machine, user will get a message when approximate algorithm is chosen to notify this choice. exact: Exact greedy algorithm. Enumerates all split candidates. approx: Approximate greedy algorithm using quantile sketch and gradient histogram.
What are the XGBoost parameters for Rapids memory manager?
Valid values of 0 (silent), 1 (warning), 2 (info), and 3 (debug). use_rmm: Whether to use RAPIDS Memory Manager (RMM) to allocate GPU memory. This option is only applicable when XGBoost is built (compiled) with the RMM plugin enabled.