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Dames Making Games is a not-for-profit videogame arts organization founded in Toronto in 2012. We run a wide range of programs and events for women, nonbinary, femme and queer folks interested in games. We support our membership by providing production space, education, advocacy, archiving, resource sharing and more collaborative practices.

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PMML 4.3 - Scorecard

A data mining model contains a set of input fields which are used to predict a certain target value. This prediction can be seen as an assessment about a prospect, a customer, or a scenario for which an outcome is predicted based on historical data. In a scorecard, input fields, also referred to as characteristics (for example, 'age'), are broken down into attributes (for example, '19-29' and '30-39' age groups or ranges) with specific partial scores associated with them. These scores represent the influence of the input attributes on the target and are readily available for inspection. Partial scores are then summed up so that an overall score can be obtained for the target value.

Scorecards are very popular in the financial industry for their interpretability and ease of implementation, and because input attributes can be mapped to a series of reason codes which provide explanations of each individual's score. Usually, the lower the overall score produced by a scorecard, the higher the chances of it triggering an adverse decision, which usually involves the referral or denial of services. Reason codes, as the name suggests, allow for an explanation of scorecard behavior and any adverse decisions generated as a consequence of the overall score. They basically answer the question: 'Why is the score low, given its input conditions?' (For inverted scoring ranges, this specification also provides for the option of returning reason codes for scores which are 'too high'. See section Scoring Procedure.)

The XML Schema for Scorecard

Definitions

  • Scorecard: The root element of an XML scorecard. Each instance of a scorecard must start with this element.
  • isScorable: This attribute indicates if the model is valid for scoring. If this attribute is true or if it is missing, then the model should be processed normally. However, if the attribute is false, then the model producer has indicated that this model is intended for information purposes only and should not be used to generate results. In order to be valid PMML, all required elements and attributes must be present, even for non-scoring models. For more details, see General Structure.
  • Attribute: Input attributes for each scorecard characteristic are defined in terms of predicates. For numeric characteristics, predicates are used to implement the mapping from a range of continuous values to a partial score. For example, age range 20 to 29 may map to partial score '15'. For categorical characteristics, predicates are used to implement the mapping of categorical values to partial scores. Note that while predicates will not (typically) overlap, the Scoring Procedure requires the ordering of Attributes to be respected, and that the first matching Attribute shall determine the partial scored value.
  • PREDICATE: The condition upon which the mapping between input attribute and partial score takes place. For more details on PREDICATE see the section on predicates in TreeModel for an explanation on how predicates are described and evaluated. In scorecard models, all the predicates defining the Attributes for a particular Characteristic must all reference a single field.

Example

Partial scores for categorical characteristic 'department'
AttributePartial Score
if value is missing-9
marketing19
engineering3
business6
Partial scores for numeric characteristic 'age'
AttributePartial Score
if value is missing-1
0-18-3
19-290
30-3912
40-18
Partial scores and reason codes for numeric characteristic 'income'
AttributePartial Score
if value is missing3
less or equal to 1000(0.03 * income) + 11
greater than 1000 and less than or equal to 15005
greater than 1500(0.01 * income) - 18

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Attribute-based reason codes for characteristic 'age'
AttributePartial ScoreReason Code
if value is missing-1RC2_1
0-18-3RC2_2
19-290RC2_3
30-3912RC2_4
40-18RC2_5
Distribution of input attributes for characteristic 'age'
AttributePartial ScoreDistribution
if value is missing-15%
0-18-314%
19-29022%
30-391234%
40-1825%

Scoring Procedure

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The scoring procedure for a scorecard is simple. Partial scores are summed up to create an overall score, the result of the scorecard. And so, for the PMML example shown above, if the input data record consists of ('engineering','25','500'), meaning department is engineering, age is 25 and income is 500, the overall score will be: 3 + 0 + 26 = 29.

In a scorecard, a single Attribute/PREDICATE per Characteristic should evaluate to TRUE. However, if more than one Attribute evaluates to TRUE, only the partial score associated with the first 'true' Attribute is used to compute the overall score. The same rule applies to reason codes. On the other hand, if not even a single Attribute/PREDICATE evaluates to TRUE for a given Characteristic, the scorecard as a whole returns an invalid value.

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Ranking Reason Codes

The ranking of reason codes can be calculated using differences either above or below the baselineScore of each characteristic. Differences below the baseline are typically used for scorecards where 'higher is better', while differences above the baseline are used with scorecards where 'lower is better'.

To properly account for the possibility that individual reason codes can be cited by multiple characteristics, the following routines are recommended for ranking the reason codes:

  1. For each unique reason code, R_1, R_2, ..., R_n, initialize points missed P_i = 0, for each i=1, ... n.
  2. For each scorecard characteristic, C_1, C_2, ..., C_m, compute point differential d_j between the realized partial score and the characteristic's baselineScore. The direction of the difference is determined by the reasonCodeAlgorithm:
    pointsBelowd_j = baselineScore_j - partialScore_j
    pointsAboved_j = partialScore_j - baselineScore_j
    Note that negative differences are possible and should be expected when the baselineMethod is other than min or max.
  3. Then, using the reason code corresponding to the scored Attribute (or for the whole Characteristic), find the appropriate index i, and add d_j to that P_i.
  4. Rank the total points, P_1, ... P_n from largest to smallest, and return the corresponding reason codes, R_1, ..., R_n in that same ordering.

In the PMML example above, reason codes would therefore be ranked in the following way: 'RC2' would be the top reason code (with a difference of 18-0=18 points), followed by 'RC1' (with 19-3=16 points). Note that the partial score associated with 'RC3', is higher than the baseline score ('26 > 10') and so it is meaningless in explaining a possible adverse decision. Since only two partial scores are lower than their respective baselineScores and given that three reason codes are to be returned in the PMML example, the second and third reason codes would be populated with the same code: 'RC1'.

Finally, if the difference between partial and baseline scores is the same for competing reason codes, the reason code to be output first will be the one associated with the Attribute or Characteristic that appears first in the PMML file, from top to bottom.

See the chapter on Outputs for details on the various types of outputs that can be returned by Scorecards.

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