Weights

Context

• This screen is only available if you designated a Weight variable in Step 2 — Definition of Variable Types.

Usage

• Click on that Weight variable in the Data panel, and the Normalize Weights checkbox appears as the only option on the screen.

• You need to determine whether to apply Normalize Weights or not:

  • If yes, the Weights will be normalized so that the total number of cases considered by BayesiaLab for machine learning is equal to the actual number of samples in the dataset.

  • If no, the Weight variable will be treated as representing the actual number of observed cases. So, a weight of 10 for one observation would be treated and counted like ten instances of that same observation. As a result, the total number of cases considered by BayesiaLab would correspond to the population from which the weight was calculated.

  • This example illustrates the situation for a survey consisting of 10 observations:

    Observation No.	Weight	Normalized Weight
    1	10	1.0
    2	12	1.2
    3	8	0.8
    4	9	0.9
    5	11	1.1
    6	13	1.3
    7	7	0.7
    8	4	0.4
    9	15	1.5
    10	11	1.1
    Sum	100	10
  • If you do not normalize, BayesiaLab would consider a sample of 100 for learning purposes and presumably find spurious relationships. This "over-counting" by a factor of 10 has the same effect as reducing the Structural Coefficient to 0.1.

  • If you normalize, BayesiaLab considers the correct proportions of the weighted samples but still only considers ten observations in total for learning purposes.

If you have specified a Weight variable, it will be taken into account in the Discretization and Aggregation algorithms.