13.2 Filter Methods for Feature Selection -- Variance Threshold (L13: Feature Selection)

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  • Опубликовано: 2 фев 2025

Комментарии • 5

  • @baharehhashemi2250
    @baharehhashemi2250 Год назад +1

    Thank youuuuuuuu

  • @alexlu7149
    @alexlu7149 Год назад

    does Weka have something like variance threshold remover?

  • @MadrissS
    @MadrissS 3 года назад +1

    Hey Sebastian, thanks for the video. As for the cons for using the Variance threshold, could we say that it doesn't take into account the scale of the variables ? And thus it would only be applicable if we normalized our dataset.

    • @SebastianRaschka
      @SebastianRaschka  3 года назад +4

      Yeah, if you don't have a normalized dataset, it would be very tricky to find a good variance threshold that works well across features. For binary variables, it is somewhat easier to reason what a good threshold might be, but for continuous variables, it's extremely hard to come up with a good number as a threshold, imho.

    • @BulkySplash169
      @BulkySplash169 3 года назад

      ​@@SebastianRaschka I started to apply variance thresholding to a dataset and was also wondering about scaling and non-binary features. It turns out variance threshold returns 1 for all normalized data. So variance threshold simply measures the standard deviation.