reclassification: Reclassification
In scoringTools: Credit Scoring Tools

Description Usage Arguments Details Value Author(s) References See Also Examples

This function performs Reject Inference using the Reclassification technique. Note that this technique has no theoretical foundation as it performs a one-step CEM algorithm.

1	reclassification(xf, xnf, yf, thresh = 0.5)

`xf`	The matrix of financed clients' characteristics to be used in the scorecard.
`xnf`	The matrix of not financed clients' characteristics to be used in the scorecard (must be the same in the same order as xf!).
`yf`	The matrix of financed clients' labels
`thresh`	The threshold to use in the Classification step, i.e. the probability above which a not financed client is considered to have a label equal to 1.

This function performs the Reclassification method on the data. When provided with labeled observations (x^\ell,y), it first fits the logistic regression model p_θ of x^\ell on y, then considers that unlabeled observations are of the expected class given by the model p_θ (this is equivalent to a CEM algorithm). It then refits a logistic regression model p_η on the whole sample.

List containing the model using financed clients only and the model produced using the Reclassification method.

Adrien Ehrhardt

Enea, M. (2015), speedglm: Fitting Linear and Generalized Linear Models to Large Data Sets, https://CRAN.R-project.org/package=speedglm Ehrhardt, A., Biernacki, C., Vandewalle, V., Heinrich, P. and Beben, S. (2018), Reject Inference Methods in Credit Scoring: a rational review,

glm, speedglm

# We simulate data from financed clients
xf <- matrix(runif(100 * 2), nrow = 100, ncol = 2)
theta <- c(2, -2)
log_odd <- apply(xf, 1, function(row) theta %*% row)
yf <- rbinom(100, 1, 1 / (1 + exp(-log_odd)))
# We simulate data from not financed clients (MCAR mechanism)
xnf <- matrix(runif(100 * 2), nrow = 100, ncol = 2)
reclassification(xf, xnf, yf)