CatScoringMethod
Related Doc:
package model
- majorityVote: categorical targets, predicted target corresponds to the category with the highest frequency of occurrence among the k-nearest neighbors.
- majorityVote: categorical targets, predicted target corresponds to the category with the highest frequency of occurrence among the k-nearest neighbors. In case of a tie, the category with the largest number of cases in the training data is the winner. If multiple categories are tied on the largest number of cases in the training data, then the category with the smallest data value (in lexical order) among the tied categories is the winner.
- weightedMajorityVote: categorical targets, predicted target corresponds to the category with the highest weighted frequency of occurrence among the k-nearest neighbors. The weights are proportional to the inverse of the distance from each k-neighbor to the query point.
- majorityVote: categorical targets, predicted target corresponds to the category with the highest frequency of occurrence among the k-nearest neighbors.
- majorityVote: categorical targets, predicted target corresponds to the category with the highest frequency of occurrence among the k-nearest neighbors. In case of a tie, the category with the largest number of cases in the training data is the winner. If multiple categories are tied on the largest number of cases in the training data, then the category with the smallest data value (in lexical order) among the tied categories is the winner.
- weightedMajorityVote: categorical targets, predicted target corresponds to the category with the highest weighted frequency of occurrence among the k-nearest neighbors. The weights are proportional to the inverse of the distance from each k-neighbor to the query point.
(Since version ) see corresponding Javadoc for more information.