Comments (5)
Had the same issue with the 0.3.0 version from CRAN, re-installing lime itself did not help, but rather the 'assertthat' package did. Thanks!
from lime.
Can I get you to try it out with the latest GitHub version? I cannot reproduce the error...
from lime.
I am having the same issue with the explain function using the Iris dataset and my own dataset using the 0.3.0 version from RCRAN. I've been attempting to download the latest github version but now see the following error upon download:
ERROR: dependency 'Rcpp' is not available for package 'lime'
- removing ~'Documents/R/win-library/3.3/lime'
Installation failed: Command failed (1)
from lime.
The CRAN version is 0.3.0? Can I get you to install that and then see if you can reproduce it. If you still get the error please post a session info
from lime.
Re-installing the package seems to have worked for me. Thanks!
from lime.
Related Issues (20)
- Exactly with same data GBM&Lime explain() works fine, but failing for RandomForest and CRF with NA/NaN/Inf in 'y'
- 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) :
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from lime.