iqnn: Iterative Quantile Binned Nearest Neighbors

In kmaurer/iqbin: Iterative Quantile Binning

Description

Function for creating iterative quantile nearest neighbors model. Bin the training data, then store the binning definitions and bin statistics to be used to estimate for future testing data.

Usage

iqnn(data, y, mod_type = "reg", bin_cols, nbins, jit = rep(0,
  length(bin_cols)), stretch = FALSE, tol = rep(0, length(bin_cols)))

Arguments

data	Data frame containing the response variable and numeric input variables from the training data
y	Name of response variable column
mod_type	Depends on response variables type: "reg" creates iqnn-regression for predicting numeric values, "class" creates iqnn-classifier for predicting categorical values
bin_cols	vector of column names of variables to iteratively bin, ordered first to last
nbins	vector of number of bins per step of iterative binning, ordered first to last
jit	vector of margins for uniform jitter to each dimension to create seperability of tied obs due to finite precision
stretch	TRUE/FALSE if will bins be given tolerance buffer
tol	vector of tolerance values to stretch each dimension for future binning

Value

list containing binned training data, binning definition, and bin statistics

See Also

Other iterative quantile nearest-neighbors functions: iqnn_cv_predict, iqnn_predict, iqnn_tune

Examples

iqnn_mod <- iqnn(data=iris, y="Species", mod_type="class",
                 bin_cols=c("Sepal.Length","Sepal.Width","Petal.Width"),
                 nbins=c(3,5,2), jit=rep(0.001,3), tol = rep(0.001,3))
str(iqnn_mod)

kmaurer/iqbin documentation built on Jan. 1, 2020, 6:48 p.m.