Weights

Context

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.

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