Changes between Initial Version and Version 2 of Ticket #1636


Ignore:
Timestamp:
02/02/18 07:54:18 (5 years ago)
Author:
gkronber
Comment:

This could also be implemented via weights for rows in the dataset.

Weights allow more fine grained control over evaluation. There is seemingly no other ticket for implementation of weighted evaluation of regression models. Therefore, I'm re-purposing this ticket.

Legend:

Unmodified
Added
Removed
Modified
  • Ticket #1636

    • Property Milestone changed from HeuristicLab 3.3.x Backlog to HeuristicLab 4.x Backlog
    • Property Summary changed from Functionality for conditional evaluation of data analysis models to Weighted evaluation of data analysis models
  • Ticket #1636 – Description

    initial v2  
    1 The fitness of a model should be calculated only on a subset of all rows of the data set for instance by declaring a variable that indicates valid rows. This can for instance be useful in certain time series modeling scenarios.
     1It should be possible to indicate weights for observations.
     2Benefits:
     3 - More important data points can be weighted more strongly
     4 - Outliers can be remove by setting the weight to zero
     5 - If we know the variance for observations we can consider non-equal variances for observations
     6 - We could more easily aggregate multiple observations and put a higher weight on it (the average has less variance).
    27
    3 In a previous version this feature has been implemented already but it has been lost in subsequent refactoring steps (see #515).