Why Up sampling over down sampling?

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I have a data of 191 samples and have created a logistic regression. I have first run the model with using the raw data and then went to upsampling. What I am not able to understand is: -

  1. Why to do upsampling before downsampling or both up and down sampling.
  2. If upsampling creates a problem of over fitting then it can be handled with scaling of the data.
  3. After up sampling or any other sampling, what are the parameters that I must look into so that I proceed with another sampling e.g. down sampling or up and down sampling?

I kindly request someone to help me understand the above.

logistic-regression machine-learning statistics
1 Answers
  1. Downsampling always means a loss of information, which is why in general downsampling is preferably avoided.
  2. Scaling is actually the best alternative. Typically the data is up-sampled as it is underrepresented in the data compared to the majority data. As many algorithm try to minimise the empirical risk -- the probability of a misclassification -- they are focusing more on the majority data. The reason for upsampling/downsampling is than as either the representation in the training data is not representative or the cost of misclassification of the minority data is much higher, e.g., in predictive maintenance. The best way to correct this is actually a cost-matrix. However, as quite a few algorithms do not have an out-ofthe-box mechnisms for cost-functions upsampling/downsampling is often used as approximation. Hence, upsampling is only to be preferred if additional "noise" can be introduced during the sampling process
  3. standard validation
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