Used only with clustering models. This function returns the likelihood that an input case will fit in the existing model.
By default, the result of the PredictCaseLikelihood function is normalized. Normalized values are typically more useful as the number of attributes in a case increase and the differences between the raw probabilities of any two cases become much smaller.
The following equation is used to calculate the normalized values, given x and y:
x = likelihood of the case based on the clustering model
y = Marginal case likelihood, calculated as the log likelihood of the case based on counting the training cases
Z = Exp( log(x) – Log(Y))
Normalized = (z/ (1+z))
The following example returns the likelihood that the specified case will occur within the clustering model that was created in the Basic Data Mining Tutorial.
SELECT PredictCaseLikelihood() AS Default_Likelihood, PredictCaseLikelihood(NORMALIZED) AS Normalized_Likelihood, PredictCaseLikelihood(NONNORMALIZED) AS Raw_Likelihood, FROM [TM Clustering] NATURAL PREDICTION JOIN (SELECT 28 AS [Age], '2-5 Miles' AS [Commute Distance], 'Graduate Degree' AS [Education], 0 AS [Number Cars Owned], 0 AS [Number Children At Home]) AS t
The difference between these results demonstrates the effect of normalization.