Why ML model produces different results despite random_state defined? And how to set global random seed for sklearn
Not every seed is the same.
Here is a definitive function that sets ALL of your seeds and you can expect complete reproducibility:

def seed_everything(seed=42):
    &quot;&quot;&quot;&quot;
    Seed everything.
    &quot;&quot;&quot;   
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True

You have to import torch, numpy etc.
UPDATE: How to set global randomseed for sklearn models:
Given that sklearn does not have its own global random seed but uses the numpy random seed we can set it globally with the above :
np.random.seed(seed)

Here is a little experiment for scipy library, analogous would be sklearn (generating random numbers-usually weights):
import numpy as np
from scipy.stats import norm
print('Without seed')
print(norm.rvs(100, size = 5))
print(norm.rvs(100, size = 5))

print('With the same seed')
np.random.seed(42) 
print(norm.rvs(100, size = 5))
np.random.seed(42) # reset the random seed back to 42
print(norm.rvs(100, size = 5))

print('Without seed')
np.random.seed(None)
print(norm.rvs(100, size = 5))
print(norm.rvs(100, size = 5))

outputing and confirming

Without seed
[100.27042599 100.9258397  100.20903163  99.88255017  99.29165699]
[100.53127275 100.17750482  98.38604284 100.74109598 101.54287085]
With the same seed
**[101.36242188 101.13410818 102.36307449  99.74043318  98.83044407]**
**[101.36242188 101.13410818 102.36307449  99.74043318  98.83044407]**
Without seed
[101.2933838  100.52176902 101.38602156 100.72865231  99.02271004]
[100.19080241  99.11010957  99.51578106 101.56403284 100.37350788]