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Y <- df$bad | |
X <- scale(df_woe$df[, -1]) | |
set.seed(2019) | |
i <- sample(seq(length(Y)), length(Y) / 4) | |
Y1 <- Y[i] | |
Y2 <- Y[-i] | |
X1 <- X[i, ] | |
X2 <- X[-i, ] | |
net1 <- grnn.fit(x = X1, y = Y1) | |
rst <- grnn.optmiz_auc(net1, lower = 1, upper = 3) | |
net2 <- grnn.fit(x = X1, y = Y1, sigma = rst$sigma) | |
xrank <- grnn.imp(net2) | |
#idx var imp1 imp2 | |
# 9 woe.bureau_score 0.03629427 0.03490435 | |
# 8 woe.rev_util 0.01150345 0.01045408 | |
# 1 woe.tot_derog 0.01033528 0.00925820 | |
# 10 woe.ltv 0.01033330 0.00910178 | |
# 11 woe.tot_income 0.00506666 0.00509438 | |
# 3 woe.age_oldest_tr 0.00430835 0.00476373 | |
# 4 woe.tot_open_tr 0.00392424 0.00523496 | |
# 2 woe.tot_tr 0.00123152 0.00215021 | |
# 5 woe.tot_rev_tr 0.00000000 0.00000000 | |
# 6 woe.tot_rev_debt 0.00000000 0.00000000 | |
# 7 woe.tot_rev_line 0.00000000 0.00000000 | |
excol <- xrank[xrank$imp1 == 0, ]$idx | |
#[1] 5 6 7 | |
MLmetrics::AUC(y_pred = grnn.parpred(net2, X2), y_true = Y2) | |
# [1] 0.7584476 | |
MLmetrics::AUC(y_pred = grnn.parpred(grnn.fit(x = X1[, -excol], y = Y1, sigma = net2$sigma), X2[, -excol]), y_true = Y2) | |
# [1] 0.7626386 | |
barplot(t(as.matrix(xrank[, 3:4])), beside = TRUE, col = c("lightcyan4", "lightcyan2"), border = NA, yaxt = "n", | |
names.arg = substring(xrank$var, 5), main = "Variable Importance Rank", cex.names = 1) |
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