parcelling | R Documentation |
This function performs Reject Inference using the Parcelling technique. Note that this technique is theoretically good in the MNAR framework although coefficients must be chosen a priori.
parcelling(
xf,
xnf,
yf,
probs = seq(0, 1, 0.25),
alpha = rep(1, length(probs) - 1)
)
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 |
probs |
The sequence of quantiles to use to make scorebands (see the vignette). |
alpha |
The user-defined coefficients to use with Parcelling (see the vignette). |
This function performs the Parcelling 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 observed bad rate in user-defined classes of predicted probabilities of p_\theta
reweighted using user-supplied weights, i.e. \hat{y}^{u} = \alpha_k T(k)
where k
denotes the group (which depends on p_\theta
) and T(k) the observed bad rate of labeled observations in this group.
It then refits a logistic regression model p_\eta
on the whole sample.
List containing the model using financed clients only and the model produced using the Parcelling 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)]
parcelling(xf, xnf, yf)
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