How to get the best feature set using tpot?

Hello,

I recently started using `tpot` for data analysis and currently in a situation where I have to select the best features (like best parameters) that leads to high performance metric. In my case, it's `f1-score`

## Context of the question

I ran `tpot classifier ` my dataset to select best performing model/pipeline.

Now what I am looking for is to get the best feature subset. I already referred issue no 710 and it doesn't help because am looking for a subset of features from all of my feature which will lead to high accuracy.

I was assuming that the best feature subset is the direct result of optimization (purpose of genetic programming) and feature importance is the derived result. Am I right?

I did see that `tpot` has `FeatureSetSelector` but why are we expected to key in the `feature subset size` as shown below? can anyone help me understand what this code does? Based on my knowledge, we provide csv file which has info feature subsets size and feature names. Out of the 4 feature subsets present in csv, we select one subset . Am I right?

```
`classifier_config_dict['tpot.builtins.FeatureSetSelector'] = {
'subset_list': ['https://raw.githubusercontent.com/EpistasisLab/tpot/master/tests/subset_test.csv'],
'sel_subset': [0,1] # select only one feature set, a list of index of subset in the list above
#'sel_subset': list(combinations(range(3), 2)) # select two feature sets
}`
```

But may I know why do we have to provide this? How can we do this like genetic algorithm optimzation approach which finds the best feature set upon trying multiple combinations.

Can someone help me with this on a simple example like Boston or Iris dataset please?