Comments (10)
I won't make any promises but it could probably be included in the next update, due within the next couple of months
from lime.
That is intentional - I have no idea how dates should be sampled in any meaningful way for the permutations
from lime.
Understood. I guess I have the issue as I have a time series model, which I understand was not what LIME was designed for, but I agree with your reasoning.
Thank you for supporting the package btw!
from lime.
Hmm - it might make sense to just hold time constant across permutations so it will give insight into why, at this time, the model behaves as it does..?
I’ll reopen and give it some more thoughts
from lime.
I'd personally recommend converting the date column to numeric. A date class is a feature to represent time, and time should be better explained by a numeric space that contains the intrinsic relationship from one point to the next, over categories that do not hold this information. Techniques like RandomForest, XGBoost, and even linear regression convert dates to numeric, as if the user wanted to convey date via a category, it should be classed as a character/factor already.
However on the topic of time series, are there any consequences when using LIME and a time series model? I understand LIME was built in mind for stationary models (for instance, decisions trees), but could LIME's sampling technique produce misleading results?
from lime.
What I'm suggesting is to hold the Date column constant, not converting it to something else. My rationale is that you're often not interested in knowing that your model is time-dependent; that lies implicit in time series. Instead you are more interested in knowing how the different variables, as they are on this specific time, have contributed.
from lime.
Ah yes, I understand what you mean now. This makes perfect sense to me now as LIME is sampling around the variables for a given epoch, and therefore it would make best sense to keep time static when doing so. Nice idea!
Any idea of when this could be implemented?
from lime.
Do you have a dummy model and data I can play with? Don't really have any real-life timeseries data to validate with...
from lime.
FYI the feature is being implemented in the date-support branch
from lime.
from lime.
Related Issues (20)
- permute_cases: Error arguments imply differing number of rows: 30000, 0
- Dealing with multiple output regression keras model
- Shiny plotOutput with plot_features from the lime package produces nothing
- Use of lime to be used in conjunction with keras model (regression)
- Error when using MLR3 for LIME
- lime/keras image classification: Input must be a vector, not a `superpixel_list` object. HOT 4
- [!] explain() does not work with ordered factors
- Flow ... through to the interactive_text_explanations
- Question about LIME results HOT 1
- Incorrect diagram in "Understanding lime"? HOT 1
- lime predicts other label than CNN
- Error in feature_distribution[[i]] : subscript out of bounds
- Error in cut.default(x[[i]], unique(explainer$bin_cuts[[i]]), labels = FALSE, : invalid number of intervals
- Error in Image Explanation
- Documentation gap concerning usage with additional libraries HOT 1
- Compatibility with tidymodels HOT 2
- Family in glmnet is always gaussian
- Release lime 0.5.3
- Error in combine_vars(data, params$plot_env, vars, drop = params$drop) :
- Allow `plot_features(cases = )` to accept integer indices even when `x` has rownames
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from lime.