fuzzy_augmentation: Fuzzy Augmentation

In adimajo/scoring: Credit Scoring Tools

Fuzzy Augmentation

Description

This function performs Reject Inference using the Fuzzy Augmentation technique. Note that this technique has no theoretical foundation and should produce (under the identifiability assumption) the same parameters' estimates than the financed clients scorecard.

Usage

fuzzy_augmentation(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 Fuzzy Augmentation 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 labels the unlabelled samples x^{u} with the predicted probabilities of p_\theta, i.e. \hat{y}^{u} = p_\theta(y|x^{u}) then refits a logistic regression model p_\eta on the whole sample.

Value

List containing the model using financed clients only and the model produced using the Fuzzy Augmentation 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)]
fuzzy_augmentation(xf, xnf, yf)

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