# Generates Best Binning Breaks `select_best_class` & `select_best_breaks` are for merging initial breaks of variables using chi-square, odds-ratio,PSI,G/B index and so on. The `get_breaks` is a simpler wrapper for `select_best_class` & `select_best_class`. ```r select_best_class( dat, x, target, breaks = NULL, occur_time = NULL, oot_pct = 0.7, pos_flag = NULL, bins_control = NULL, sp_values = NULL, ... ) select_best_breaks( dat, x, target, breaks = NULL, pos_flag = NULL, sp_values = NULL, occur_time = NULL, oot_pct = 0.7, bins_control = NULL, ... ) ``` ## Arguments - `dat`: A data frame with x and target. - `x`: The name of variable to process. - `target`: The name of target variable. - `breaks`: Splitting points for an independent variable. Default is NULL. - `occur_time`: The name of the variable that represents the time at which each observation takes place. - `oot_pct`: The percentage of Actual and Expected set for PSI calculating. - `pos_flag`: The value of positive class of target variable, default: "1". - `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. - `sp_values`: A list of special value. - `...`: Other parameters. ## Returns A list of breaks for x. ## Details The folloiwing is the list of Reference Principles * 1.The increasing or decreasing trend of variables is consistent with the actual business experience.(The percent of Non-monotonic intervals of which are not head or tail is less than 0.35) * 2.Maximum 10 intervals for a single variable. * 3.Each interval should cover more than 2 * 4.Each interval needs at least 30 or 1 * 5.Combining the values of blank, missing or other special value into the same interval called missing. * 6.The difference of Chi effect size between intervals should be at least 0.02 or more. * 7.The difference of absolute odds ratio between intervals should be at least 0.1 or more. * 8.The difference of positive rate between intervals should be at least 1/10 of the total positive rate. * 9.The difference of G/B index between intervals should be at least 15 or more. * 10.The PSI of each interval should be less than 0.1. ## Examples ```r #equal sample size breaks equ_breaks = cut_equal(dat = UCICreditCard[, "PAY_AMT2"], g = 10) # select best bins bins_control = list(bins_num = 10, bins_pct = 0.02, b_chi = 0.02, b_odds = 0.1, b_psi = 0.05, b_or = 0.15, mono = 0.3, odds_psi = 0.1, kc = 1) select_best_breaks(dat = UCICreditCard, x = "PAY_AMT2", breaks = equ_breaks, target = "default.payment.next.month", occur_time = "apply_date", sp_values = NULL, bins_control = bins_control) ``` ## See Also `get_tree_breaks`, `cut_equal`, `get_breaks`

select_best_class function