object CatScoringMethod extends Enumeration
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- type CatScoringMethod = Value
- class Val extends Value with Serializable
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- @SerialVersionUID()
- abstract class Value extends Ordered[Value] with Serializable
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- class ValueSet extends AbstractSet[Value] with SortedSet[Value] with SortedSetOps[Value, SortedSet, ValueSet] with StrictOptimizedIterableOps[Value, Set, ValueSet] with Serializable
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- final def Value(i: Int, name: String): Value
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- final def Value(name: String): Value
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- final def apply(x: Int): Value
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- final def isInstanceOf[T0]: Boolean
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- val majorityVote: Value
- 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.
- final def maxId: Int
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- var nextId: Int
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- var nextName: Iterator[String]
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- def values: ValueSet
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- val weightedMajorityVote: Value
- 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.
- final def withName(s: String): Value
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- implicit object ValueOrdering extends Ordering[Value]
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