Comments (2)
biofuture
commented on August 17, 2024
For many of the currently generated biological data, they are all with this feature, such as metabolomics, cytokines data. The machine has a detection limit for the concentration if your tool can support this type of imputation, I think many people would like to use your tool in their study.
Best wishes!
from gain.
jsyoon0823
commented on August 17, 2024
Unfortunately, I do not think GAIN (and other data-driven methods) can be used for this setting.
It is very hard for data-driven methods to estimate something out of the distribution from the observed data.
I suggest that instead of using the imputation, it may be good to use the missingness as the additional feature (binary feature) and put the missing parts as 0.
from gain.
Related Issues (20)
- How to decide Missingness Mechanism HOT 1
- Differences with the paper HOT 1
- Using GAIN in inductive mode HOT 1
- Changing only missing values? and scoring? HOT 1
- Why not both L_G and L_D relevant to V(D,G)? HOT 1
- Could you please provide Requirements.txt file HOT 1
- My dataset is 203454KB, I can't get the dataset after filling, because my dataset is too big? It gives some mistakes. HOT 1
- mixed (categorical and numerical) data HOT 3
- Model for the MNIST dataset HOT 1
- alpha HOT 1
- original data HOT 1
- Hyperparameters training HOT 3
- hyperparameters HOT 3
- RMSE is not stable HOT 1
- RMSE HOT 1
- Hint matrix HOT 1
- Why isn't the loss calculated only with b_i=0 values of the Hints. HOT 2
- No split training and testing sets？ HOT 2
- Training Query HOT 1
- about minibatch HOT 1
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