scorecardpy 库woebin函数详解

之前对scorecardpy 库woebin函数进行了一些介绍，对于其中的参数，现在挨个进行讲解。

先看一下底层函数的参数说明（比较长可以直接跳过）：

def woebin(dt, y, x=None, 
           var_skip=None, breaks_list=None, special_values=None, 
           stop_limit=0.1, count_distr_limit=0.05, bin_num_limit=8, 
           # min_perc_fine_bin=0.02, min_perc_coarse_bin=0.05, max_num_bin=8, 
           positive="bad|1", no_cores=None, print_step=0, method="tree",
           ignore_const_cols=True, ignore_datetime_cols=True, 
           check_cate_num=True, replace_blank=True, 
           save_breaks_list=None, **kwargs):
    '''
    WOE Binning
    ------
    `woebin` generates optimal binning for numerical, factor and categorical 
    variables using methods including tree-like segmentation or chi-square 
    merge. woebin can also customizing breakpoints if the breaks_list or 
    special_values was provided.
    
    The default woe is defined as ln(Distr_Bad_i/Distr_Good_i). If you 
    prefer ln(Distr_Good_i/Distr_Bad_i), please set the argument `positive` 
    as negative value, such as '0' or 'good'. If there is a zero frequency 
    class when calculating woe, the zero will replaced by 0.99 to make the 
    woe calculable.
    
    Params
    ------
    dt: A data frame with both x (predictor/feature) and y (response/label) variables.
    y: Name of y variable.
    x: Name of x variables. Default is None. If x is None, 
      then all variables except y are counted as x variables.
    var_skip: Name of variables that will skip for binning. Defaults to None.
    breaks_list: List of break points, default is None. 
      If it is not None, variable binning will based on the 
      provided breaks.
    special_values: the values specified in special_values 
      will be in separate bins. Default is None.
    count_distr_limit: The minimum percentage of final binning