auto_model_accuracy: auto model accuracy
In autostats: Auto Stats

auto_model_accuracy

R Documentation

auto model accuracy

Description

Runs a cross validated xgboost and regularized linear regression, and reports accuracy metrics. Automatically determines whether the provided formula is a regression or classification.

Usage

auto_model_accuracy(
  data,
  formula,
  ...,
  n_folds = 4,
  as_flextable = TRUE,
  include_linear = FALSE,
  theme = "tron",
  seed = 1,
  mtry = 1,
  trees = 15L,
  min_n = 1L,
  tree_depth = 6L,
  learn_rate = 0.3,
  loss_reduction = 0,
  sample_size = 1,
  stop_iter = 10L,
  counts = FALSE,
  penalty = 0.015,
  mixture = 0.35
)

Arguments

`data`	data frame
`formula`	formula
`...`	any other params for xgboost
`n_folds`	number of cross validation folds
`as_flextable`	if FALSE, returns a tibble
`include_linear`	if TRUE includes a regularized linear model
`theme`	make_flextable theme
`seed`	seed
`mtry`	# Randomly Selected Predictors (xgboost: colsample_bynode) (type: numeric, range 0 - 1) (or type: integer if `count = TRUE`)
`trees`	# Trees (xgboost: nrounds) (type: integer, default: 15L)
`min_n`	Minimal Node Size (xgboost: min_child_weight) (type: integer, default: 1L); [typical range: 2-10] Keep small value for highly imbalanced class data where leaf nodes can have smaller size groups. Otherwise increase size to prevent overfitting outliers.
`tree_depth`	Tree Depth (xgboost: max_depth) (type: integer, default: 6L); Typical values: 3-10
`learn_rate`	Learning Rate (xgboost: eta) (type: double, default: 0.3); Typical values: 0.01-0.3
`loss_reduction`	Minimum Loss Reduction (xgboost: gamma) (type: double, default: 0.0); range: 0 to Inf; typical value: 0 - 20 assuming low-mid tree depth
`sample_size`	Proportion Observations Sampled (xgboost: subsample) (type: double, default: 1.0); Typical values: 0.5 - 1
`stop_iter`	# Iterations Before Stopping (xgboost: early_stop) (type: integer, default: 15L) only enabled if validation set is provided
`counts`	if `TRUE` specify `mtry` as an integer number of cols. Default `FALSE` to specify `mtry` as fraction of cols from 0 to 1
`penalty`	linear regularization parameter
`mixture`	linear model parameter, combines l1 and l2 regularization