Following are the steps that occur in LIME's algorithm
I have been trying to read and understand why this process is followed. My questions are around some of steps.
1) Permutation is done based on the distributions of the input variables from actual data. Hence, fake data is created. Question - why do we need to create fake data? if we need observations around the local region of the observation we are predicting for, can't we just take it from the actual data using a distance metric? followed by building simple model on this small subset of the data
2) How exactly do we use the similarity score here from the fake data(simulations) and actual data?
3) In step 6, Fit a simple model to the permuted data, explaining the complex model outcome with the m features from the permuted data weighted by its similarity to the original observation what exactly are we modelling after selecting best 'm' variables?
How would the simulations turn out if i built a model on say a big data set of 100000k rows and 12 cols, however for prediction i have just one row. Will simulation replicate the same row multiple times and then go and get the similar rows from actual big dataset, build a model on that or simulated data set and then derive coefficients?