Type: Package
Title: Auto Stats
Version: 0.4.1
Maintainer: Harrison Tietze <harrison4192@gmail.com>
Description: Automatically do statistical exploration. Create formulas using 'tidyselect' syntax, and then determine cross-validated model accuracy and variable contributions using 'glm' and 'xgboost'. Contains additional helper functions to create and modify formulas. Has a flagship function to quickly determine relationships between categorical and continuous variables in the data set.
Encoding: UTF-8
Imports: dplyr, stringr, tidyselect, purrr, janitor, tibble, rlang, stats, rlist, broom, magrittr, ggeasy, ggplot2, jtools, gtools, ggthemes, patchwork, tidyr, xgboost, parsnip, recipes, rsample, tune, workflows, framecleaner, presenter, yardstick, dials, party, data.table, nnet, recosystem, Ckmeans.1d.dp, broom.mixed, igraph
RoxygenNote: 7.3.1
URL: https://harrison4192.github.io/autostats/, https://github.com/Harrison4192/autostats
BugReports: https://github.com/Harrison4192/autostats/issues
Suggests: knitr, rmarkdown, forcats, parallel, doParallel, hardhat, flextable, glmnet, ggstance, Matrix, BBmisc, readr, lubridate, ranger, XICOR
VignetteBuilder: knitr
License: MIT + file LICENSE
NeedsCompilation: no
Packaged: 2024-06-03 05:56:13 UTC; harrisontietze
Author: Harrison Tietze [aut, cre]
Depends: R (≥ 3.5.0)
Repository: CRAN
Date/Publication: 2024-06-04 09:44:44 UTC

Pipe operator

Description

See magrittr::%>% for details.

Value

lhs %>% rhs

auto anova

Description

A wrapper around lm and anova to run a regression of a continuous variable against categorical variables. Used for determining the whether the mean of a continuous variable is statistically significant amongst different levels of a categorical variable.

Usage

auto_anova(
  data,
  ...,
  baseline = c("mean", "median", "first_level", "user_supplied"),
  user_supplied_baseline = NULL,
  sparse = FALSE,
  pval_thresh = 0.1
)

Arguments

data

a data frame

...

tidyselect specification or cols

baseline

choose from "mean", "median", "first_level", "user_supplied". what is the baseline to compare each category to? can use the mean and median of the target variable as a global baseline

user_supplied_baseline

if intercept is "user_supplied", can enter a numeric value

sparse

default FALSE; if true returns a truncated output with only significant results

pval_thresh

control significance level for sparse output filtering

Details

Columns can be inputted as unquoted names or tidyselect. Continuous and categorical variables are automatically determined. If no character or factor column is present, the column with the lowest amount of unique values will be considered the categorical variable.

Description of columns in the output

target

continuous variables

predictor

categorical variables

level

levels in the categorical variables

estimate

difference between level target mean and baseline

target_mean

target mean per level

n

rows in predictor level

std.error

standard error of target in predictor level

level_p.value

p.value for t.test of whether target mean differs significantly between level and baseline

level_significance

level p.value represented by stars

predictor_p.value

p.value for significance of entire predictor given by F test

predictor_significance

predictor p.value represented by stars

conclusion

text interpretation of tests

Value

data frame

Examples


iris %>%
auto_anova(tidyselect::everything()) -> iris_anova1


iris_anova1 %>%
print(width = Inf)

auto_boxplot

Description

Wraps geom_boxplot to simplify creating boxplots.

Usage

auto_boxplot(
  .data,
  continuous_outcome,
  categorical_variable,
  categorical_facets = NULL,
  alpha = 0.3,
  width = 0.15,
  color_dots = "black",
  color_box = "red"
)

Arguments

.data

data

continuous_outcome

continuous y variable. unquoted column name

categorical_variable

categorical x variable. unquoted column name

categorical_facets

categorical facet variable. unquoted column name

alpha

alpha points

width

width of jitter

color_dots

dot color

color_box

box color

Value

ggplot

Examples


iris %>%
auto_boxplot(continuous_outcome = Petal.Width, categorical_variable = Species)

auto correlation

Description

Finds the correlation between numeric variables in a data frame, chosen using tidyselect. Additional parameters for the correlation test can be specified as in cor.test

Usage

auto_cor(
  .data,
  ...,
  use = c("pairwise.complete.obs", "all.obs", "complete.obs", "everything",
    "na.or.complete"),
  method = c("pearson", "kendall", "spearman", "xicor"),
  include_nominals = TRUE,
  max_levels = 5L,
  sparse = TRUE,
  pval_thresh = 0.1
)

Arguments

.data

data frame

...

tidyselect cols

use

method to deal with na. Default is to remove rows with NA

method

correlation method. default is pearson, but also supports xicor.

include_nominals

logicals, default TRUE. Dummify nominal variables?

max_levels

maximum numbers of dummies to be created from nominal variables

sparse

logical, default TRUE. Filters and arranges cor table

pval_thresh

threshold to filter out weak correlations

Details

includes the asymmetric correlation coefficient xi from xicor

Value

data frame of correlations

Examples


iris %>%
auto_cor()

# don't use sparse if you're interested in only one target variable
iris %>%
auto_cor(sparse = FALSE) %>%
dplyr::filter(x == "Petal.Length")

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; defaults to .75; (xgboost: colsample_bynode) (type: numeric, range 0 - 1) (or type: integer if count = TRUE)

trees

# Trees (xgboost: nrounds) (type: integer, default: 500L)

min_n

Minimal Node Size (xgboost: min_child_weight) (type: integer, default: 2L); [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: 7L); Typical values: 3-10

learn_rate

Learning Rate (xgboost: eta) (type: double, default: 0.05); Typical values: 0.01-0.3

loss_reduction

Minimum Loss Reduction (xgboost: gamma) (type: double, default: 1.0); range: 0 to Inf; typical value: 0 - 20 assuming low-mid tree depth

sample_size

Proportion Observations Sampled (xgboost: subsample) (type: double, default: .75); 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

Value

a table

auto t test

Description

Performs a t.test on 2 populations for numeric variables.

Usage

auto_t_test(data, col, ..., var_equal = FALSE, abbrv = TRUE)

Arguments

data

dataframe

col

a column with 2 categories representing the 2 populations

...

numeric variables to perform t.test on. Default is to select all numeric variables

var_equal

default FALSE; t.test parameter

abbrv

default TRUE; remove some extra columns from output

Value

dataframe

Examples



iris %>%
 dplyr::filter(Species != "setosa") %>%
 auto_t_test(col = Species)

auto_tune_xgboost

Description

Automatically tunes an xgboost model using grid or bayesian optimization

Usage

auto_tune_xgboost(
  .data,
  formula,
  tune_method = c("grid", "bayes"),
  event_level = c("first", "second"),
  n_fold = 5L,
  n_iter = 100L,
  seed = 1,
  save_output = FALSE,
  parallel = TRUE,
  trees = tune::tune(),
  min_n = tune::tune(),
  mtry = tune::tune(),
  tree_depth = tune::tune(),
  learn_rate = tune::tune(),
  loss_reduction = tune::tune(),
  sample_size = tune::tune(),
  stop_iter = tune::tune(),
  counts = FALSE,
  tree_method = c("auto", "exact", "approx", "hist", "gpu_hist"),
  monotone_constraints = 0L,
  num_parallel_tree = 1L,
  lambda = 1,
  alpha = 0,
  scale_pos_weight = 1,
  verbosity = 0L
)

Arguments

.data

dataframe

formula

formula

tune_method

method of tuning. defaults to grid

event_level

for binary classification, which factor level is the positive class. specify "second" for second level

n_fold

integer. n folds in resamples

n_iter

n iterations for tuning (bayes); paramter grid size (grid)

seed

seed

save_output

FASLE. If set to TRUE will write the output as an rds file

parallel

default TRUE; If set to TRUE, will enable parallel processing on resamples for grid tuning

trees

# Trees (xgboost: nrounds) (type: integer, default: 500L)

min_n

Minimal Node Size (xgboost: min_child_weight) (type: integer, default: 2L); [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.

mtry

# Randomly Selected Predictors; defaults to .75; (xgboost: colsample_bynode) (type: numeric, range 0 - 1) (or type: integer if count = TRUE)

tree_depth

Tree Depth (xgboost: max_depth) (type: integer, default: 7L); Typical values: 3-10

learn_rate

Learning Rate (xgboost: eta) (type: double, default: 0.05); Typical values: 0.01-0.3

loss_reduction

Minimum Loss Reduction (xgboost: gamma) (type: double, default: 1.0); range: 0 to Inf; typical value: 0 - 20 assuming low-mid tree depth

sample_size

Proportion Observations Sampled (xgboost: subsample) (type: double, default: .75); 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

tree_method

xgboost tree_method. default is auto. reference: tree method docs

monotone_constraints

an integer vector with length of the predictor cols, of -1, 1, 0 corresponding to decreasing, increasing, and no constraint respectively for the index of the predictor col. reference: monotonicity docs.

num_parallel_tree

should be set to the size of the forest being trained. default 1L

lambda

[default=.5] L2 regularization term on weights. Increasing this value will make model more conservative.

alpha

[default=.1] L1 regularization term on weights. Increasing this value will make model more conservative.

scale_pos_weight

[default=1] Control the balance of positive and negative weights, useful for unbalanced classes. if set to TRUE, calculates sum(negative instances) / sum(positive instances). If first level is majority class, use values < 1, otherwise normally values >1 are used to balance the class distribution.

verbosity

[default=1] Verbosity of printing messages. Valid values are 0 (silent), 1 (warning), 2 (info), 3 (debug).

Details

Default is to tune all 7 xgboost parameters. Individual parameter values can be optionally fixed to reduce tuning complexity.

Value

workflow object

Examples





iris %>%
 framecleaner::create_dummies() -> iris1

iris1 %>%
 tidy_formula(target = Petal.Length) -> petal_form

iris1 %>%
 rsample::initial_split() -> iris_split

iris_split %>%
 rsample::analysis() -> iris_train

iris_split %>%
 rsample::assessment() -> iris_val

## Not run: 
iris_train %>%
 auto_tune_xgboost(formula = petal_form, n_iter = 10,
 parallel = FALSE, tune_method = "grid", mtry = .5) -> xgb_tuned

xgb_tuned %>%
 parsnip::fit(iris_train) %>%
 parsnip::extract_fit_engine() -> xgb_tuned_fit

xgb_tuned_fit %>%
 tidy_predict(newdata = iris_val, form = petal_form) -> iris_val1

## End(Not run)

Plot Variable Contributions

Description

Return a variable importance plot and coefficient plot from a linear model. Used to easily visualize the contributions of explanatory variables in a supervised model

Usage

auto_variable_contributions(data, formula, scale = TRUE)

Arguments

data

dataframe

formula

formula

scale

logical. If FALSE puts coefficients on original scale

Value

a ggplot object

Examples


iris %>%
framecleaner::create_dummies() %>%
auto_variable_contributions(
 tidy_formula(., target = Petal.Width)
 )

iris %>%
auto_variable_contributions(
tidy_formula(., target = Species)
)

cap_outliers

Description

Caps the outliers of a numeric vector by percentiles. Also outputs a plot of the capped distribution

Usage

cap_outliers(x, q = 0.05, type = c("both", "upper", "lower"))

Arguments

x

numeric vector

q

decimal input to the quantile function to set cap. default .05 caps at the 95 and 5th percentile

type

chr vector. where to cap: both, upper, or lower

Value

numeric vector

Examples


cap_outliers(iris$Petal.Width)

create monotone constraints

Description

helper function to create the integer vector to pass to the monotone_constraints argument in xgboost

Usage

create_monotone_constraints(
  .data,
  formula,
  decreasing = NULL,
  increasing = NULL
)

Arguments

.data

dataframe, training data for tidy_xgboost

formula

formula used for tidy_xgboost

decreasing

character vector or tidyselect regular expression to designate decreasing cols

increasing

character vector or tidyselect regular expression to designate increasing cols

Value

a named integer vector with entries of 0, 1, -1

Examples




iris %>%
framecleaner::create_dummies(Species) -> iris_dummy

iris_dummy %>%
 tidy_formula(target= Petal.Length) -> petal_form

iris_dummy %>%
 create_monotone_constraints(petal_form,
                             decreasing = tidyselect::matches("Petal|Species"),
                             increasing = "Sepal.Width")

determine pred type

Description

determine pred type

Usage

determine_pred_type(x, original_col)

Arguments

x

a vector

original_col

logical. is numeric

Value

a string

eval_preds

Description

Automatically evaluates predictions created by tidy_predict. No need to supply column names.

Usage

eval_preds(.data, ..., softprob_model = NULL)

Arguments

.data

dataframe as a result of tidy_predict

...

additional metrics from yarstick to be calculated

softprob_model

character name of the model used to create multiclass probabilities

Value

tibble of summarized metrics

charvec to formula

Description

takes the lhs and rhs of a formula as character vectors and outputs a formula

Usage

f_charvec_to_formula(lhs, rhs)

Arguments

lhs

lhs atomic chr vec

rhs

rhs chr vec

Value

formula

Examples


lhs <- "Species"
rhs <- c("Petal.Width", "Custom_Var")

f_charvec_to_formula(lhs, rhs)

Formula_rhs to chr vec

Description

Accepts a formula and returns the rhs as a character vector.

Usage

f_formula_to_charvec(f, include_lhs = FALSE, .data = NULL)

Arguments

f

formula

include_lhs

FALSE. If TRUE, appends lhs to beginning of vector

.data

dataframe for names if necessary

Value

chr vector

Examples


iris %>%
tidy_formula(target = Species, tidyselect::everything()) -> f

f

f %>%
f_formula_to_charvec()

Modify Formula

Description

Modify components of a formula by adding / removing vars from the rhs or replacing the lhs.

Usage

f_modify_formula(
  f,
  rhs_remove = NULL,
  rhs_add = NULL,
  lhs_replace = NULL,
  negate = TRUE
)

Arguments

f

formula

rhs_remove

regex or character vector for dropping variables from the rhs

rhs_add

character vector for adding variables to rhs

lhs_replace

string to replace formula lhs if supplied

negate

should rhs_remove keep or remove the specified vars. Set to FALSE to keep

Value

formula

Examples


iris %>%
tidy_formula(target = Species, tidyselect::everything()) -> f

f

f %>%
  f_modify_formula(
rhs_remove = c("Petal.Width", "Sepal.Length"),
rhs_add = "Custom_Variable"
)

f %>%
  f_modify_formula(
rhs_remove = "Petal",
lhs_replace = "Petal.Length"
)

get params

Description

s3 method to extract params of a model with names consistent for use in the 'autostats' package

Usage

get_params(model, ...)

## S3 method for class 'xgb.Booster'
get_params(model, ...)

## S3 method for class 'workflow'
get_params(model, ...)

Arguments

model

a model

...

additional arguments

Value

list of params

Examples


iris %>%
  framecleaner::create_dummies() -> iris_dummies

iris_dummies %>%
  tidy_formula(target = Petal.Length) -> p_form

iris_dummies %>%
  tidy_xgboost(p_form, mtry = .5, trees = 5L, loss_reduction = 2, sample_size = .7) -> xgb

## reuse these parameters to find the cross validated error

rlang::exec(auto_model_accuracy, data = iris_dummies, formula = p_form, !!!get_params(xgb))

impute_recosystem

Description

Imputes missing values of a numeric matrix using stochastic gradient descent. recosystem

Usage

impute_recosystem(
  .data,
  lrate = c(0.05, 0.1),
  costp_l1 = c(0, 0.05),
  costq_l1 = c(0, 0.05),
  costp_l2 = c(0, 0.05),
  costq_l2 = c(0, 0.05),
  nthread = 8,
  loss = "l2",
  niter = 15,
  verbose = FALSE,
  nfold = 4,
  seed = 1
)

Arguments

.data

long format data frame

lrate

learning rate

costp_l1

l1 cost p

costq_l1

l1 cost q

costp_l2

l2 cost p

costq_l2

l2 cost q

nthread

nthreads

loss

loss function. also can use "l1"

niter

training iterations for tune

verbose

show training loss?

nfold

folds for tune validation

seed

seed for randomness

Details

input is a long data frame with 3 columns: ID col, Item col (the column names from pivoting longer), and the ratings (values from pivoting longer)

pre-processing generally requires pivoting a wide user x item matrix to long format. The missing values from the matrix must be retained as NA values in the rating column. The values will be predicted and filled in by the algorithm. Output is a long data frame with the same number of rows as input, but no missing values.

This function automatically tunes the recosystem learner before applying. Parameter values can be supplied for tuning. To avoid tuning, use single values for the parameters.

Value

long format data frame

plot glm coefs

Description

plot glm coefs

Usage

plot_coefs_glm(glm, font = c("", "HiraKakuProN-W3"))

Arguments

glm

glm

font

font

Value

plot

plot ctree

Description

plot ctree

Usage

plot_ctree(ctree_obj, plot_type = c("sample", "box", "bar"))

Arguments

ctree_obj

output of tidy_ctree

plot_type

type of plot

Value

decision tree plot

plot cforest variable importance

Description

plot cforest variable importance

Usage

plot_varimp_cforest(cfar, font = c("", "HiraKakuProN-W3"))

Arguments

cfar

cforest model

font

font

Value

ggplot

Plot varimp xgboost

Description

recommended parameters to control;

Usage

plot_varimp_xgboost(
  xgb,
  top_n = 10L,
  aggregate = NULL,
  as_table = FALSE,
  formula = NULL,
  measure = c("Gain", "Cover", "Frequency"),
  ...
)

Arguments

xgb

xgb.Booster model

top_n

top n important variables

aggregate

a character vector. Predictors containing the string will be aggregated, and renamed to that string.

as_table

logical, default FALSE. If TRUE returns importances in a data frame

formula

formula for the model. Use to provide original names if xgboost is scrambling the names internally

measure

choose between Gain, Cover, or Frequency for xgboost importance measure

...

additional arguments for xgb.ggplot.importance

Details

top_n

number of features to include in the graph

Value

ggplot

tidy conditional inference forest

Description

Runs a conditional inference forest.

Usage

tidy_cforest(data, formula, seed = 1)

Arguments

data

dataframe

formula

formula

seed

seed integer

Value

a cforest model

Examples


iris %>%
tidy_cforest(
  tidy_formula(., Petal.Width)
) -> iris_cfor

iris_cfor

iris_cfor %>%
visualize_model()

tidy ctree

Description

tidy conditional inference tree. Creates easily interpretable decision tree models that be shown with the visualize_model function. Statistical significance required for a split , and minimum necessary samples in a terminal leaf can be controlled to create the desired tree visual.

Usage

tidy_ctree(.data, formula, minbucket = 7L, mincriterion = 0.95, ...)

Arguments

.data

dataframe

formula

formula

minbucket

minimum amount of samples in terminal leaves, default is 7

mincriterion

(1 - alpha) value between 0 -1, default is .95. lowering this value creates more splits, but less significant

...

optional parameters to ctree_control

Value

a ctree object

Examples


iris %>%
tidy_formula(., Sepal.Length) -> sepal_form

iris %>%
tidy_ctree(sepal_form) %>%
visualize_model()

iris %>%
tidy_ctree(sepal_form, minbucket = 30) %>%
visualize_model(plot_type = "box")

tidy formula construction

Description

Takes a dataframe and allows for use of tidyselect to construct a formula.

Usage

tidy_formula(data, target, ...)

Arguments

data

dataframe

target

lhs

...

tidyselect. rhs

Value

a formula

Examples


iris %>%
tidy_formula(Species, tidyselect::everything())

tidy glm

Description

Runs either a linear regression, logistic regression, or multinomial classification. The model is automatically determined based off the nature of the target variable.

Usage

tidy_glm(data, formula)

Arguments

data

dataframe

formula

formula

Value

glm model

Examples


# linear regression
iris %>%
tidy_glm(
tidy_formula(., target = Petal.Width)) -> glm1

glm1

glm1 %>%
visualize_model()

# multinomial classification

tidy_formula(iris, target = Species) -> species_form

iris %>%
tidy_glm(species_form) -> glm2


glm2 %>%
visualize_model()

#  logistic regression
iris %>%
dplyr::filter(Species != "setosa") %>%
tidy_glm(species_form) -> glm3

suppressWarnings({
glm3 %>%
visualize_model()})

tidy predict

Description

tidy predict

Usage

tidy_predict(
  model,
  newdata,
  form = NULL,
  olddata = NULL,
  bind_preds = FALSE,
  ...
)

## S3 method for class 'Rcpp_ENSEMBLE'
tidy_predict(model, newdata, form = NULL, ...)

## S3 method for class 'glm'
tidy_predict(model, newdata, form = NULL, ...)

## Default S3 method:
tidy_predict(model, newdata, form = NULL, ...)

## S3 method for class 'BinaryTree'
tidy_predict(model, newdata, form = NULL, ...)

## S3 method for class 'xgb.Booster'
tidy_predict(
  model,
  newdata,
  form = NULL,
  olddata = NULL,
  bind_preds = FALSE,
  ...
)

## S3 method for class 'lgb.Booster'
tidy_predict(
  model,
  newdata,
  form = NULL,
  olddata = NULL,
  bind_preds = FALSE,
  ...
)

Arguments

model

model

newdata

dataframe

form

the formula used for the model

olddata

training data set

bind_preds

set to TURE if newdata is a dataset without any labels, to bind the new and old data with the predictions under the original target name

...

other parameters to pass to predict

Value

dataframe

Examples

iris %>%
 framecleaner::create_dummies(Species) -> iris_dummy

iris_dummy %>%
 tidy_formula(target= Petal.Length) -> petal_form

iris_dummy %>%
 tidy_xgboost(
   petal_form,
   trees = 20,
   mtry = .5
 )  -> xg1


xg1 %>%
 tidy_predict(newdata = iris_dummy, form = petal_form) %>%
 head()

tidy shap

Description

plot and summarize shapley values from an xgboost model

Usage

tidy_shap(model, newdata, form = NULL, ..., top_n = 12, aggregate = NULL)

Arguments

model

xgboost model

newdata

dataframe similar to model input

form

formula used for model

...

additional parameters for shapley value

top_n

top n features

aggregate

a character vector. Predictors containing the string will be aggregated, and renamed to that string.

Details

returns a list with the following entries

shap_tbl

: table of shaply values

shap_summary

: table summarizing shapley values. Includes correlation between shaps and feature values.

swarmplot

: one plot showing the relation between shaps and features

scatterplots

: returns the top 9 most important features as determined by sum of absolute shapley values, as a facetted scatterplot of feature vs shap

Value

list

tidy xgboost

Description

Accepts a formula to run an xgboost model. Automatically determines whether the formula is for classification or regression. Returns the xgboost model.

Usage

tidy_xgboost(
  .data,
  formula,
  ...,
  mtry = 0.75,
  trees = 500L,
  min_n = 2L,
  tree_depth = 7L,
  learn_rate = 0.05,
  loss_reduction = 1,
  sample_size = 0.75,
  stop_iter = 15L,
  counts = FALSE,
  tree_method = c("auto", "exact", "approx", "hist", "gpu_hist"),
  monotone_constraints = 0L,
  num_parallel_tree = 1L,
  lambda = 0.5,
  alpha = 0.1,
  scale_pos_weight = 1,
  verbosity = 0L,
  validate = TRUE,
  booster = c("gbtree", "gblinear")
)

Arguments

.data

dataframe

formula

formula

...

additional parameters to be passed to set_engine

mtry

# Randomly Selected Predictors; defaults to .75; (xgboost: colsample_bynode) (type: numeric, range 0 - 1) (or type: integer if count = TRUE)

trees

# Trees (xgboost: nrounds) (type: integer, default: 500L)

min_n

Minimal Node Size (xgboost: min_child_weight) (type: integer, default: 2L); [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: 7L); Typical values: 3-10

learn_rate

Learning Rate (xgboost: eta) (type: double, default: 0.05); Typical values: 0.01-0.3

loss_reduction

Minimum Loss Reduction (xgboost: gamma) (type: double, default: 1.0); range: 0 to Inf; typical value: 0 - 20 assuming low-mid tree depth

sample_size

Proportion Observations Sampled (xgboost: subsample) (type: double, default: .75); 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

tree_method

xgboost tree_method. default is auto. reference: tree method docs

monotone_constraints

an integer vector with length of the predictor cols, of -1, 1, 0 corresponding to decreasing, increasing, and no constraint respectively for the index of the predictor col. reference: monotonicity docs.

num_parallel_tree

should be set to the size of the forest being trained. default 1L

lambda

[default=.5] L2 regularization term on weights. Increasing this value will make model more conservative.

alpha

[default=.1] L1 regularization term on weights. Increasing this value will make model more conservative.

scale_pos_weight

[default=1] Control the balance of positive and negative weights, useful for unbalanced classes. if set to TRUE, calculates sum(negative instances) / sum(positive instances). If first level is majority class, use values < 1, otherwise normally values >1 are used to balance the class distribution.

verbosity

[default=1] Verbosity of printing messages. Valid values are 0 (silent), 1 (warning), 2 (info), 3 (debug).

validate

default TRUE. report accuracy metrics on a validation set.

booster

defaults to 'gbtree' for tree boosting but can be set to 'gblinear'

Details

In binary classification the target variable must be a factor with the first level set to the event of interest. A higher probability will predict the first level.

reference for parameters: xgboost docs

Value

xgb.Booster model

Examples


options(rlang_trace_top_env = rlang::current_env())


# regression on numeric variable

iris %>%
 framecleaner::create_dummies(Species) -> iris_dummy

iris_dummy %>%
 tidy_formula(target= Petal.Length) -> petal_form

iris_dummy %>%
 tidy_xgboost(
   petal_form,
   trees = 20,
   mtry = .5
 )  -> xg1


xg1 %>%
 tidy_predict(newdata = iris_dummy, form = petal_form) -> iris_preds

iris_preds %>%
 eval_preds()

visualize model

Description

s3 method to automatically visualize the output of of a model object. Additional arguments can be supplied for the original function. Check the corresponding plot function documentation for any custom arguments.

Usage

visualize_model(model, ...)

## S3 method for class 'RandomForest'
visualize_model(model, ..., method)

## S3 method for class 'BinaryTree'
visualize_model(model, ..., method)

## S3 method for class 'glm'
visualize_model(model, ..., method)

## S3 method for class 'multinom'
visualize_model(model, ..., method)

## S3 method for class 'xgb.Booster'
visualize_model(
  model,
  top_n = 10L,
  aggregate = NULL,
  as_table = FALSE,
  formula = NULL,
  measure = c("Gain", "Cover", "Frequency"),
  ...,
  method
)

## Default S3 method:
visualize_model(model, ..., method)

Arguments

model

a model

...

additional arguments

method

choose amongst different visualization methods

top_n

return top n elements

aggregate

= summarize

as_table

= false, table or graph,

formula

= formula,

measure

= c("Gain", "Cover", "Frequency")

Value

a plot