# Generates Best Breaks for Binning `get_breaks` is for generating optimal binning for numerical and nominal variables. The `get_breaks_all` is a simpler wrapper for `get_breaks`. ```r get_breaks_all( dat, target = NULL, x_list = NULL, ex_cols = NULL, pos_flag = NULL, occur_time = NULL, oot_pct = 0.7, best = TRUE, equal_bins = FALSE, cut_bin = "equal_depth", g = 10, sp_values = NULL, tree_control = list(p = 0.05, cp = 1e-06, xval = 5, maxdepth = 10), bins_control = list(bins_num = 10, bins_pct = 0.05, b_chi = 0.05, b_odds = 0.1, b_psi = 0.05, b_or = 0.15, mono = 0.3, odds_psi = 0.2, kc = 1), parallel = FALSE, note = FALSE, save_data = FALSE, file_name = NULL, dir_path = tempdir(), ... ) get_breaks( dat, x, target = NULL, pos_flag = NULL, best = TRUE, equal_bins = FALSE, cut_bin = "equal_depth", g = 10, sp_values = NULL, occur_time = NULL, oot_pct = 0.7, tree_control = NULL, bins_control = NULL, note = FALSE, ... ) ``` ## Arguments - `dat`: A data frame with x and target. - `target`: The name of target variable. - `x_list`: A list of x variables. - `ex_cols`: A list of excluded variables. Default is NULL. - `pos_flag`: The value of positive class of target variable, default: "1". - `occur_time`: The name of the variable that represents the time at which each observation takes place. - `oot_pct`: Percentage of observations retained for overtime test (especially to calculate PSI). Defualt is 0.7 - `best`: Logical, if TRUE, merge initial breaks to get optimal breaks for binning. - `equal_bins`: Logical, if TRUE, equal sample size initial breaks generates.If FALSE , tree breaks generates using desison tree. - `cut_bin`: A string, if equal_bins is TRUE, 'equal_depth' or 'equal_width', default is 'equal_depth'. - `g`: Integer, number of initial bins for equal_bins. - `sp_values`: A list of missing values. - `tree_control`: the list of tree parameters. * `p` the minimum percent of observations in any terminal \ node. 0 \< p\< 1; 0.01 to 0.1 usually work. * `cp` complexity parameter. the larger, the more conservative the algorithm will be. 0 \< cp\< 1 ; 0.0001 to 0.0000001 usually work. * `xval` number of cross-validations.Default: 5 * `max_depth` maximum depth of a tree. Default: 10 - `bins_control`: the list of parameters. * `bins_num` The maximum number of bins. 5 to 10 usually work. Default: 10 * `bins_pct` The minimum percent of observations in any bins. 0 \< bins_pct \< 1 , 0.01 to 0.1 usually work. Default: 0.02 * `b_chi` The minimum threshold of chi-square merge. 0 \< b_chi\< 1; 0.01 to 0.1 usually work. Default: 0.02 * `b_odds` The minimum threshold of odds merge. 0 \< b_odds \< 1; 0.05 to 0.2 usually work. Default: 0.1 * `b_psi` The maximum threshold of PSI in any bins. 0 \< b_psi \< 1 ; 0 to 0.1 usually work. Default: 0.05 * `b_or` The maximum threshold of G/B index in any bins. 0 \< b_or \< 1 ; 0.05 to 0.3 usually work. Default: 0.15 * `odds_psi` The maximum threshold of Training and Testing G/B index PSI in any bins. 0 \< odds_psi \< 1 ; 0.01 to 0.3 usually work. Default: 0.1 * `mono` Monotonicity of all bins, the larger, the more nonmonotonic the bins will be. 0 \< mono \< 0.5 ; 0.2 to 0.4 usually work. Default: 0.2 * `kc` number of cross-validations. 1 to 5 usually work. Default: 1 - `parallel`: Logical, parallel computing or not. Default is FALSE. - `note`: Logical.Outputs info.Default is TRUE. - `save_data`: Logical, save results in locally specified folder. Default is TRUE - `file_name`: File name that save results in locally specified folder. Default is "breaks_list". - `dir_path`: Path to save results. Default is "./variable" - `...`: Additional parameters. - `x`: The Name of an independent variable. ## Returns A table containing a list of splitting points for each independent variable. ## Examples ```r #controls tree_control = list(p = 0.02, cp = 0.000001, xval = 5, maxdepth = 10) bins_control = list(bins_num = 10, bins_pct = 0.02, b_chi = 0.02, b_odds = 0.1, b_psi = 0.05, b_or = 15, mono = 0.2, odds_psi = 0.1, kc = 5) # get categrory variable breaks b = get_breaks(dat = UCICreditCard[1:1000,], x = "MARRIAGE", target = "default.payment.next.month", occur_time = "apply_date", sp_values = list(-1, "missing"), tree_control = tree_control, bins_control = bins_control) # get numeric variable breaks b2 = get_breaks(dat = UCICreditCard[1:1000,], x = "PAY_2", target = "default.payment.next.month", occur_time = "apply_date", sp_values = list(-1, "missing"), tree_control = tree_control, bins_control = bins_control) # get breaks of all predictive variables b3 = get_breaks_all(dat = UCICreditCard[1:1000,], target = "default.payment.next.month", x_list = c("MARRIAGE","PAY_2"), occur_time = "apply_date", ex_cols = "ID", sp_values = list(-1, "missing"), tree_control = tree_control, bins_control = bins_control, save_data = FALSE) ``` ## See Also `get_tree_breaks`, `cut_equal`, `select_best_class`, `select_best_breaks`

get_breaks_all function