confusion_results {fastLogisticRegressionWrap}

Binary Confusion Table and Errors

Description

Provides a binary confusion table and error metrics

Usage

confusion_results(yhat, ybin, skip_argument_checks = FALSE)

Arguments

yhat

The binary predictions

ybin

The true binary responses

skip_argument_checks

If TRUE it does not check this function's arguments for appropriateness. It is not recommended unless you truly need speed and thus the default is FALSE.

Value

A list of raw results

Examples

library(MASS); data(Pima.te)
ybin = as.numeric(Pima.te$type == "Yes")
flr = fast_logistic_regression(
  Xmm = model.matrix(~ . - type, Pima.te), 
  ybin = ybin
)
phat = predict(flr, model.matrix(~ . - type, Pima.te))
confusion_results(phat > 0.5, ybin)