Description Usage Arguments Details Value
The starting point for any binnr scorecard. The bin function
prepares variables for scorecard modeling by returning a
Scorecard-class
object that is easily manipulated.
1 2 |
data |
|
y |
Response variable. Currently, only binary performance is supported. |
w |
Weight variable. If omitted, a weight variable of all 1s is created. |
min.iv |
The minimum information value to split a continuous variable. |
min.cnt |
The minumum number of observations that must be in a bin after any split. |
min.res |
The minimum number of response observations that must be in a bin after any split. |
mono |
Monotonicity constraint
|
max.bin |
The maximum number of bins into which |
exceptions |
A numeric vector of values to be excluded from the discretization process. |
bin
Is the workhorse of binnr Depending on the input
variable class type, it will behave differently. For numeric and integer
variables it will discretize using information value. The returned object is
of class Continuous
. Factors are summarized and passed through as-is.
The returned object for factors if of class Discrete
.
The operations that can be performed on a bin depend on its class. Continuous
bins are subject to the constratins passed into the bin functions.
Furthermore, only adjacent levels may be collapsed. Discrete bins are not
constrained by the bin
function parameters and non-adjacent levels
may be collapsed.
Missing values are always held out of binning. They may be combined only if the variable was binned as discrete.
A Scorecard
object with data
variables discretized.
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