GBMModel

optional character string specifying the name of the
distribution to use or list with a component <code>name</code> specifying the
distribution and any additional parameters needed. Set automatically
according to the class type of the response variable.

distribution

n.trees

maximum depth of variable interactions.

interaction.depth

minimum number of observations in the trees terminal
nodes.

n.minobsinnode

shrinkage parameter applied to each tree in the expansion.

shrinkage

fraction of the training set observations randomly
selected to propose the next tree in the expansion.

bag.fraction

Fits generalized boosted regression models.

Meta-package for statistical and machine learning with a unified
interface for model fitting, prediction, performance assessment, and
presentation of results.  Approaches for model fitting and prediction of
numerical, categorical, or censored time-to-event outcomes include
traditional regression models, regularization methods, tree-based methods,
support vector machines, neural networks, ensembles, data preprocessing,
filtering, and model tuning and selection.  Performance metrics are provided
for model assessment and can be estimated with independent test sets, split
sampling, cross-validation, or bootstrap resampling.  Resample estimation
can be executed in parallel for faster processing and nested in cases of
model tuning and selection.  Modeling results can be summarized with
descriptive statistics; calibration curves; variable importance; partial
dependence plots; confusion matrices; and ROC, lift, and other performance
curves.

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GBMModel: Generalized Boosted Regression Model

Description

Usage

Arguments

Value

Details

See Also

Examples