twins: Twins

In adimajo/scoring: Credit Scoring Tools

Twins

Description

This function performs Reject Inference using the Twins technique. Note that this technique has no theoretical foundation.

Usage

twins(xf, xnf, yf)

Arguments

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

Details

This function performs the Twins method on the data. When provided with labeled observations (x^\ell,y), it first fits the logistic regression model p_\theta of x^\ell on y, then fits the logistic regression model p_\omega of X on the binomial random variable denoting the observation of the data Z. We use predictions of both models on the labeled observations to construct a "meta"-score based on logistic regression which predicted probabilities are used to reweight samples and construct the final score p_\eta.

Value

List containing the model using financed clients only, the model of acceptance and the model produced using the Twins method.

Author(s)

Adrien Ehrhardt

References

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,

See Also

glm, speedglm

Examples

# We simulate data from financed clients
df <- generate_data(n = 100, d = 2)
xf <- df[, -ncol(df)]
yf <- df$y
# We simulate data from not financed clients (MCAR mechanism)
xnf <- generate_data(n = 100, d = 2)[, -ncol(df)]
twins(xf, xnf, yf)

adimajo/scoring documentation built on March 7, 2024, 11:18 p.m.