partial_dependence_plot {creditmodel} | R Documentation |
partial_dependence_plot
is for generating a partial dependence plot.
get_partial_dependence_plots
is for ploting partial dependence of all vairables in x_list.
partial_dependence_plot(model, x, x_train, n.trees = NULL) get_partial_dependence_plots( model, x_train, x_list, n.trees = NULL, dir_path = getwd(), save_data = TRUE, plot_show = FALSE, parallel = FALSE )
model |
A data frame of training with predicted prob or score. |
x |
The name of an independent variable. |
x_train |
A data.frame with independent variables. |
n.trees |
Number of trees for best.iter of gbm. |
x_list |
Names of independent variables. |
dir_path |
The path for periodically saved graphic files. |
save_data |
Logical, save results in locally specified folder. Default is FALSE. |
plot_show |
Logical, show model performance in current graphic device. Default is FALSE. |
parallel |
Logical, parallel computing. Default is FALSE. |
sub = cv_split(UCICreditCard, k = 30)[[1]] dat = UCICreditCard[sub,] dat = re_name(dat, "default.payment.next.month", "target") dat = data_cleansing(dat, target = "target", obs_id = "ID", occur_time = "apply_date", miss_values = list("", -1)) train_test = train_test_split(dat, split_type = "OOT", prop = 0.7, occur_time = "apply_date") dat_train = train_test$train dat_test = train_test$test x_list = c("PAY_0", "LIMIT_BAL", "PAY_AMT5", "PAY_3", "PAY_2") Formula = as.formula(paste("target", paste(x_list, collapse = ' + '), sep = ' ~ ')) set.seed(46) lr_model = glm(Formula, data = dat_train[, c("target", x_list)], family = binomial(logit)) #plot partial dependency of one variable partial_dependence_plot(model = lr_model, x ="LIMIT_BAL", x_train = dat_train) #plot partial dependency of all variables pd_list = get_partial_dependence_plots(model = lr_model, x_list = x_list[1:2], x_train = dat_train, save_data = FALSE,plot_show = TRUE)