Nothing

```
#' Tweaked predict to take into account levels that are not in the training set but in the test set and remove these rows.
#'
#' This function applies the logistic regression predict after carefully removing observations of categorical features' levels absent from the training set..
#' @param model The logistic regression model to use to predict on a test set.
#' @param df The whole test set which class has to be predicted.
#' @param c_iter The segment considered (given by the tree).
#' @return A dataframe of predictions (in rows: the observations, in cols: the class probabilities) given by the model given in input, eventually tweaked if some levels are unknown to it.
#' @keywords internal
#' @author Adrien Ehrhardt
predict_tweaked <- function(model, df, c_iter) {
pred = tryCatch(
stats::predict(model, newdata = df, type = "response"),
error = function(e) {
data_w = df
data_wo = df
encodage = caret::dummyVars( ~ ., data = df)
# Levels not in the training set but in the test set are removed
for (var in setdiff(encodage$facVars, c("c_map", "c_hat"))) {
if (length(levels(df[, var])[!(levels(df[, var]) %in% (levels(factor(df[df$c_hat == levels(df$c_hat)[c_iter], var]))))]) > 0) {
data_w <-
data_w[data_w[, var] %in% levels(factor(df[df$c_hat == levels(df$c_hat)[c_iter], var])), ]
data_wo <-
data_wo[data_wo[, var] %in% (levels(df[, var])[!(levels(df[, var]) %in% (levels(factor(df[df$c_hat == levels(df$c_hat)[c_iter], var]))))]), ]
}
}
pred_w = stats::predict(model, newdata = data_w, type = "response")
pred_wo = rep(mean(df[df$c_hat == c_iter, "y"], na.rm = T), nrow(data_wo))
pred = c(pred_w, pred_wo)
names(pred) = c(rownames(data_w), rownames(data_wo))
return(pred[order(as.numeric(names(pred)))][rownames(df)])
}
)
return(pred)
}
```

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