I am doing multiclass/multilabel text classification. I trying to get rid of the "ConvergenceWarning". 

When I tuned the **max\_iter** from default to **4000**, the warning is disappeared. However, my model accuracy is reduced from **78** to **75**. 

```
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

logreg = Pipeline([('vect', CountVectorizer()),
            ('tfidf', TfidfTransformer()),
            ('clf', LogisticRegression(n_jobs=1, C=1e5, solver='lbfgs',multi_class='ovr' ,random_state=0, class_weight='balanced' )),
           ])
logreg.fit(X_train, y_train)

y_pred = logreg.predict(X_test)

print('Logistic Regression Accuracy %s' % accuracy_score(y_pred, y_test))

cv_score = cross_val_score(logreg, train_tfidf, y_train, cv=10, scoring='accuracy')
print("CV Score : Mean : %.7g | Std : %.7g | Min : %.7g | Max : %.7g" % (np.mean(cv_score),np.std(cv_score),np.min(cv_score),np.max(cv_score)))

```

Why my accuracy is reducing when max\_iter =4000?
Is there any other way to fix 
**\* "ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations. "of iterations.", ConvergenceWarning)" \***