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Hi, i have trouble to use 3d input data of my dnn. Does lime support 3d input somehow?

makovez

Using **LimeTabularExplainer** class, I am trying to understand what local model is used to explain a single instance. According to documentation, **sklearn.linear_model.Ridge** is the default model. If this is case,...

PaschalisLagias

I saw that the sampling in Lime `lime_tabular.py` is doing the following: `data = data * scale + instance_sample` or `data = data * scale + mean` May I know...

trevor-yap

Calculate r2 score

3

Is it possible to calculate the r2 score of the surrogate model? Can this feature be added?

SamiurRahman1

Cannot use lime with grayscale images

3

Hello Marco! I have an issue with grayscale images. If I understand correctly, when using grayscale what I gotta do is: 1 - Convert image (1 x img_width x img_height...

rEstela

OOM with Bert Tokenizer

2

Hello, I am using lime to get interpretation about a classification problem. First, I am using Flaubert Tokenizer (also I tried different tokenizer and had the same problem) to transfer...

ma-batita

Hope everyone is holding on tight, we're soon at Christmas! Hi, as I have already made a post about the issue in detail over at stackexchange, I'll just post the...

Ostlimpa

Add predict_fn_accept_dense_only to explain_instance when input is sparse but model takes dense

1

Add predict_fn_accept_dense_only to explain_instance. When data_row (of explain_instance) is scipy.sparse.matrix but the model was NOT trained with scipy.sparse.matrix, the predict_fn_accept_dense_only convert inverse to dense before call predict_fn (and then back...

BinDong314

background/filter dataset in LIME

1

Hi LIME community, Thanks for the help or any lead in advance. One thing we are looking for is to provide a background data for LIME at the time of...

BinDong314

top_label

1

The top_label is the number of classes? My problem has 4 classes, even if I put a top_label=10 it always returns 4

ElineideSilva