twins | R Documentation |
This function performs Reject Inference using the Twins technique. Note that this technique has no theoretical foundation.
twins(xf, xnf, yf)
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 |
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
.
List containing the model using financed clients only, the model of acceptance and the model produced using the Twins 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
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)
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