[FIXED] Python - constrain values within interquartile range

Issue

I am able to get this to run piecemeal, but when I tried a for loop to run it across all the models I am getting an error. It looks like I am giving a list of 1 rather than the item. How do I get it to provide the item? Or is there a better way to do this?

my_list = [LogisticRegression(max_iter=1000, random_state=1),LinearDiscriminantAnalysis(), SVC(probability = True,random_state=1)]

for i in my_list:
    rfecv = RFECV(estimator=[i],step=1)
    rfecv.fit(x,y)
    print(" ")
    print(i)
    for j, col, in zip(range(df.shape[1]), df.columns):
      print(f'{col} selected= {rfecv.support_[1]} rank= {rfecv.ranking_[1]}')

The error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-164-f885654e3d56> in <module>
      3 for i in my_list:
      4     rfecv = RFECV(estimator=[i],step=1)
----> 5     rfecv.fit(x,y)
      6     print(" ")
      7     print(i)

1 frames
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py in check_scoring(estimator, scoring, allow_none)
    448         raise TypeError(
    449             "estimator should be an estimator implementing 'fit' method, %r was passed"
--> 450             % estimator
    451         )
    452     if isinstance(scoring, str):

TypeError: estimator should be an estimator implementing 'fit' method, [LogisticRegression(max_iter=1000, random_state=1)] was passed

Solution

You are putting i in a list, so instead of passing [i] to the fit function just pass i:

my_list = [LogisticRegression(max_iter=1000, random_state=1),LinearDiscriminantAnalysis(), SVC(probability = True,random_state=1)]

for i in my_list:
    rfecv = RFECV(estimator=i,step=1)
    rfecv.fit(x,y)
    print(" ")
    print(i)
    for j, col, in zip(range(df.shape[1]), df.columns):
      print(f'{col} selected= {rfecv.support_[1]} rank= {rfecv.ranking_[1]}')

Answered By - Adam Jaamour