glmLite: Bare bones decision tree derived from 'rpart'

In egenn/rtemis: Machine Learning and Visualization

Bare bones decision tree derived from rpart

Description

A super-stripped down decision tree for when space and performance are critical

Usage

glmLite(
  x,
  y,
  weights = NULL,
  method = c("glmnet", "cv.glmnet", "lm.ridge", "allSubsets", "forwardStepwise",
    "backwardStepwise", "glm", "sgd", "solve"),
  alpha = 0,
  lambda = 0.01,
  lambda.seq = NULL,
  cv.glmnet.nfolds = 5,
  which.cv.glmnet.lambda = c("lambda.min", "lambda.1se"),
  nbest = 1,
  nvmax = 8,
  sgd.model = "glm",
  sgd.model.control = list(lambda1 = 0, lambda2 = 0),
  sgd.control = list(method = "ai-sgd"),
  save.fitted = FALSE,
  ...
)

Arguments

x	Feature matrix or data.frame. Will be coerced to data.frame for method = "allSubsets", "forwardStepwise", or "backwardStepwise"
y	Outcome
weights	Float, vector: Case weights
method	Character: Method to use: "glm": uses stats::lm.wfit "glmnet": uses glmnet::glmnet "cv.glmnet": uses glmnet:cv.glmnet "lm.ridge": uses MASS::lm.ridge "allsubsets": uses leaps::regsubsets with method = "exhaustive" "forwardStepwise": uses leaps::regsubsets with method = "forward" "backwardStepwise": uses leaps::regsubsets with method = "backward" "sgd": uses sgd::sgd "solve": uses base::solve "none": fits no model and returns all zeroes, for programming convenience in special cases
alpha	Float: alpha for method = glmnet or cv.glmnet.
lambda	Float: The lambda value for glmnet, cv.glmnet, lm.ridge Note: For glmnet and cv.glmnet, this is the lambda used for prediction. Training uses lambda.seq.
lambda.seq	Float, vector: lambda sequence for glmnet and cv.glmnet.
cv.glmnet.nfolds	Integer: Number of folds for cv.glmnet
which.cv.glmnet.lambda	Character: Whitch lambda to pick from cv.glmnet: "lambda.min": Lambda that gives minimum cross-validated error;
nbest	Integer: For method = "allSubsets", number of subsets of each size to record. Default = 1
nvmax	Integer: For method = "allSubsets", maximum number of subsets to examine.
sgd.model	Character: Model to use for method = "sgd".
sgd.model.control	List: model.control list to pass to sgd::sgd
sgd.control	List: sgd.control list to pass to sgd::sgd "lambda.1se": Largest lambda such that error is within 1 s.e. of the minimum.
save.fitted	Logical: If TRUE, save fitted values in output. Default = FALSE
...	Additional arguments to pass to lincoef

Author(s)

E.D. Gennatas

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.