Question 5 - other result

Module 6. Ensemble of models Wrap-up quiz
#1

hello,

with code similar to the correction i got different result

from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=300)
result_rf = cross_validate(rf, data, target,
                              cv=10, scoring= ['accuracy', 'balanced_accuracy'],
                              n_jobs=2)

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import KFold

gb = GradientBoostingClassifier(n_estimators=300)
for i in range(10):
  cv = KFold(n_splits=10, shuffle=True, random_state=i )
  result_rf = cross_validate(rf, data, target,
                              cv=cv, scoring= ['accuracy', 'balanced_accuracy'],
                              n_jobs=2)
  result_gb  = cross_validate(gb, data, target,
                              cv=cv, scoring= ['accuracy', 'balanced_accuracy'],
                              n_jobs=2)
  print(result_rf['test_balanced_accuracy'].mean())
  print(result_gb['test_balanced_accuracy'].mean())
  print(result_gb['test_balanced_accuracy'].mean() -result_rf['test_balanced_accuracy'].mean())
#2

Can you edit your post to make it more readable using markdown, see Mastering Markdown · GitHub Guides for more details.

For example if I use this:

```py
for i in range(10):
    print('i:', i)
```

It will render as:

for i in range(10):
    print('i:', i)
#3

it’s done

#4

thanks i found the mistake
you can delete all this chat if you want

#5

I am curious: what mistake did you find?