Criterion
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package model
There are many many possible ways of applying rulesets, but three useful approaches are covered.
There are many many possible ways of applying rulesets, but three useful approaches are covered.
- firstHit: First firing rule is chosen as the predicted class, and the confidence is the confidence of that rule. If further predictions and confidences are required, a search for the next firing rule that chooses a different predicted class is made, and so on.
- weightedSum: Calculate the total weight for each class by summing the weights for each firing rule which predicts that class. The prediction with the highest total weight is then selected. The confidence is the total confidence of the winning class divided by the number of firing rules. If further predictions and confidences are required, the process is repeated to find the class with the second highest total weight, and so on. Note that if two or more classes are assigned the same weight, the winning class is the one that appears first in the data dictionary values.
- weightedMax: Select the firing rule with the highest weight. The confidence returned is the confidence of the selected rule. Note that if two firing rules have the same weight, the rule that occurs first in the ruleset is chosen.
There are many many possible ways of applying rulesets, but three useful approaches are covered.
There are many many possible ways of applying rulesets, but three useful approaches are covered.
- firstHit: First firing rule is chosen as the predicted class, and the confidence is the confidence of that rule. If further predictions and confidences are required, a search for the next firing rule that chooses a different predicted class is made, and so on.
- weightedSum: Calculate the total weight for each class by summing the weights for each firing rule which predicts that class. The prediction with the highest total weight is then selected. The confidence is the total confidence of the winning class divided by the number of firing rules. If further predictions and confidences are required, the process is repeated to find the class with the second highest total weight, and so on. Note that if two or more classes are assigned the same weight, the winning class is the one that appears first in the data dictionary values.
- weightedMax: Select the firing rule with the highest weight. The confidence returned is the confidence of the selected rule. Note that if two firing rules have the same weight, the rule that occurs first in the ruleset is chosen.
There are many many possible ways of applying rulesets, but three useful approaches are covered.
There are many many possible ways of applying rulesets, but three useful approaches are covered.
- firstHit: First firing rule is chosen as the predicted class, and the confidence is the confidence of that rule. If further predictions and confidences are required, a search for the next firing rule that chooses a different predicted class is made, and so on.
- weightedSum: Calculate the total weight for each class by summing the weights for each firing rule which predicts that class. The prediction with the highest total weight is then selected. The confidence is the total confidence of the winning class divided by the number of firing rules. If further predictions and confidences are required, the process is repeated to find the class with the second highest total weight, and so on. Note that if two or more classes are assigned the same weight, the winning class is the one that appears first in the data dictionary values.
- weightedMax: Select the firing rule with the highest weight. The confidence returned is the confidence of the selected rule. Note that if two firing rules have the same weight, the rule that occurs first in the ruleset is chosen.