training_data_checker {Rforestry}R Documentation

Training data check

Description

Check the input to forestry constructor

Usage

training_data_checker(
  x,
  y,
  ntree,
  replace,
  sampsize,
  mtry,
  nodesizeSpl,
  nodesizeAvg,
  nodesizeStrictSpl,
  nodesizeStrictAvg,
  minSplitGain,
  maxDepth,
  interactionDepth,
  splitratio,
  nthread,
  middleSplit,
  doubleTree,
  linFeats,
  monotonicConstraints,
  featureWeights,
  deepFeatureWeights,
  observationWeights,
  linear,
  hasNas
)

Arguments

x

A data frame of all training predictors.

y

A vector of all training responses.

ntree

The number of trees to grow in the forest. The default value is 500.

replace

An indicator of whether sampling of training data is with replacement. The default value is TRUE.

sampsize

The size of total samples to draw for the training data. If sampling with replacement, the default value is the length of the training data. If samplying without replacement, the default value is two-third of the length of the training data.

mtry

The number of variables randomly selected at each split point. The default value is set to be one third of total number of features of the training data.

nodesizeSpl

Minimum observations contained in terminal nodes. The default value is 3.

nodesizeAvg

Minimum size of terminal nodes for averaging dataset. The default value is 3.

nodesizeStrictSpl

Minimum observations to follow strictly in terminal nodes. The default value is 1.

nodesizeStrictAvg

Minimum size of terminal nodes for averaging dataset to follow strictly. The default value is 1.

minSplitGain

Minimum loss reduction to split a node further in a tree.

maxDepth

Maximum depth of a tree. The default value is 99.

interactionDepth

All splits at or above interaction depth must be on variables that are not weighting variables (as provided by the interactionVariables argument)

splitratio

Proportion of the training data used as the splitting dataset. It is a ratio between 0 and 1. If the ratio is 1, then essentially splitting dataset becomes the total entire sampled set and the averaging dataset is empty. If the ratio is 0, then the splitting data set is empty and all the data is used for the averaging data set (This is not a good usage however since there will be no data available for splitting).

nthread

Number of threads to train and predict the forest. The default number is 0 which represents using all cores.

middleSplit

if the split value is taking the average of two feature values. If false, it will take a point based on a uniform distribution between two feature values. (Default = FALSE)

doubleTree

if the number of tree is doubled as averaging and splitting data can be exchanged to create decorrelated trees. (Default = FALSE)

linFeats

Specify which features to split linearly on when using linear (defaults to use all numerical features)

monotonicConstraints

Specifies monotonic relationships between the continuous features and the outcome. Supplied as a vector of length p with entries in 1,0,-1 which 1 indicating an increasing monotonic relationship, -1 indicating a decreasing monotonic relationship, and 0 indicating no relationship. Constraints supplied for categorical will be ignored.

featureWeights

weights used when subsampling features for nodes above or at interactionDepth.

deepFeatureWeights

weights used when subsampling features for nodes below interactionDepth.

observationWeights

These denote the weights for each training observation which determines how likely the observation is to be selected in each bootstrap sample. This option is not allowed when sampling is done without replacement.

linear

Fit the model with a ridge regression or not

hasNas

indicates if there is any missingness in x.


[Package Rforestry version 0.11.1.0 Index]