Inconsistent Results For Logistic Regressions across multiple computers

### Describe the bug

Hey all - 

I'm working on teaching some students some logistic regressions and noticed different computers can produce slightly different intercepts/coefs. At first I thought it was maybe environment differences, but I have been able to reproduce the variances when accounting for the various packages/interpreters. 

I can create a new environment on computer 1 (windows 10, intel 8th gen CPU), computer 2 (windows 10, intel 11th gen CPU), and a coworker's M1 MacBook - all three produce different results. I thought maybe minute differences in Numpy's Openblas or MKL could be the culprit but that yielded the same varying results. I've tried not splitting the data, different random states, different C values, different solvers... 

For the purposes of reproducibility, I ran this is exact code & uploaded the data to Google sheets:

```python
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

bank_df = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSGfjG4mq1_4HS4iwRN7EZK6YHzPDi8HpB_giY7kiqbDZRsRNjbfhuQ2J6xkHGk1YVYN9H0TxOf2tgw/pub?gid=1909291157&single=true&output=csv')
bank_df.drop(columns=['ID', 'ZIP_Code'], inplace=True)

bank_df['Education'] = bank_df['Education'].astype('category')
new_categories = {1: 'Undergrad', 2: 'Graduate', 3: 'Advanced/Professional'}
bank_df.Education.cat.rename_categories(new_categories, inplace=True)
bank_df = pd.get_dummies(bank_df, prefix_sep='_', drop_first=True)

y = bank_df['Personal_Loan']
X = bank_df.drop(columns=['Personal_Loan'])

# partition data
train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.4, random_state=1)

# fit a logistic regression (set penalty=l2 and C=1e42 to avoid regularization)
logit_reg = LogisticRegression(penalty="l2", C=1e42, solver='liblinear', random_state=0)
logit_reg.fit(train_X, train_y)
```

All versions of all other packages are held constant. I received this results across the three computers: 


| values | Computer 1 with Sklearn 1.1.2 | Computer 2 with Sklearn 1.1.2 | M1 Macbook with Sklearn 1.1.1 |
|---|---|---|---|
|Intercept|	-12.4919578542609|	-12.6123809362746	| -12.4934360611768|
|Age coef|	-0.037738|	-0.032577	|-0.037685|
|Experience coef|	0.039255|	0.034171|0.039202|
|Income coef|	0.058843|	0.058837	|0.058844|
|Family coef|	0.612243|	0.613192	|0.612251|

I have tried many other things that all produce slightly different results that are not captured in the above table. Multicollinearity is present within the data, but I would still expect consistent results.

I understand the results are effectively the same and this produces little real world impacts. However, when a student is comparing results and they see 12.4 vs 12.6 which is a decently large difference. I think some of these differences are too large to just be contributed to floating-point computations -- especially for size of the data. I assume I'm missing something, but I'm reaching the end of my rope on troubleshooting. Thoughts / expected behavior? 

### Steps/Code to Reproduce

```python
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression

bank_df = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSGfjG4mq1_4HS4iwRN7EZK6YHzPDi8HpB_giY7kiqbDZRsRNjbfhuQ2J6xkHGk1YVYN9H0TxOf2tgw/pub?gid=1909291157&single=true&output=csv')
bank_df.drop(columns=['ID', 'ZIP_Code'], inplace=True)

bank_df['Education'] = bank_df['Education'].astype('category')
new_categories = {1: 'Undergrad', 2: 'Graduate', 3: 'Advanced/Professional'}
bank_df.Education.cat.rename_categories(new_categories, inplace=True)
bank_df = pd.get_dummies(bank_df, prefix_sep='_', drop_first=True)

y = bank_df['Personal_Loan']
X = bank_df.drop(columns=['Personal_Loan'])

# partition data
train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.4, random_state=1)

# fit a logistic regression (set penalty=l2 and C=1e42 to avoid regularization)
logit_reg = LogisticRegression(penalty="l2", C=1e42, solver='liblinear')
logit_reg.fit(train_X, train_y)
```

### Expected Results

| values | Computer 1 with Sklearn 1.1.2 | Computer 2 with Sklearn 1.1.2 | M1 Macbook with Sklearn 1.1.1 |
|---|---|---|---|
|Intercept|	-12.4919578542609|	-12.6123809362746	| -12.4934360611768|
|Age coef|	-0.037738|	-0.032577	|-0.037685|
|Experience coef|	0.039255|	0.034171|0.039202|
|Income coef|	0.058843|	0.058837	|0.058844|
|Family coef|	0.612243|	0.613192	|0.612251|

### Actual Results

n/a

### Versions

```shell
System:
    python: 3.9.13 (main, Aug 25 2022, 23:51:50) [MSC v.1916 64 bit (AMD64)]
executable: C:\Users\Nick\.conda\envs\sktest\python.exe
   machine: Windows-10-10.0.19044-SP0

Python dependencies:
      sklearn: 1.1.2
          pip: 22.2.2
   setuptools: 63.4.1
        numpy: 1.23.1
        scipy: 1.9.1
       Cython: None
       pandas: 1.4.4
   matplotlib: None
       joblib: 1.1.0
threadpoolctl: 2.2.0

Built with OpenMP: True

threadpoolctl info:
       filepath: C:\Users\Nick\.conda\envs\sktest\Lib\site-packages\sklearn\.libs\vcomp140.dll
         prefix: vcomp
       user_api: openmp
   internal_api: openmp
        version: None
    num_threads: 12

       filepath: C:\Users\Nick\.conda\envs\sktest\Library\bin\mkl_rt.1.dll
         prefix: mkl_rt
       user_api: blas
   internal_api: mkl
        version: 2021.4-Product
    num_threads: 6
threading_layer: intel
```

COMMENT:
@MujassimJamal thanks for the input. I still am incredibly suspicious that differences in approximated weights & rounding could yield the nontrivial differences in the numbers above. If we saw a difference in the hundred thousandths then sure, but a .1-.2+ difference seems suspect. Do you feel otherwise? 


Iterations/learning rate were account for in many, many tests (and @glemaitre's test seemingly). I attempted to account for computational environments as stated in my original post. I'm aware that differences in the actual CPU architecture could yield some infinitely small differences, but same conda, python, blas, mkl, etc.

Labels: Bug, module:linear_model, Needs Investigation