K cross validation with different results everytime

Question

All my models are initialized with the below:

def intiailize_clf_models(self):
    model = RandomForestClassifier(random_state=42)
    self.clf_models.append((model))

    model = ExtraTreesClassifier(random_state=42)
    self.clf_models.append((model))

    model = MLPClassifier(random_state=42)
    self.clf_models.append((model))

    model = LogisticRegression(random_state=42)
    self.clf_models.append((model))

    model = xgb.XGBClassifier(random_state=42)
    self.clf_models.append((model))

    model = lgb.LGBMClassifier(random_state=42)
    self.clf_models.append((model))

Which loops through the models and performs k fold cross validation with :

def kfold_cross_validation(self):
    clf_models = self.get_models()
    models = []
    self.results = {}

    for model in clf_models:
        self.current_model_name = model.__class__.__name__
        cross_validate = cross_val_score(model, self.xtrain, self.ytrain, cv=4)
        self.mean_cross_validation_score = cross_validate.mean()
        print("Kfold cross validation for", self.current_model_name)
        self.results[self.current_model_name] = self.mean_cross_validation_score
        models.append(model)

Anytime i run this cross validation, i get a different result even after i have set a random state on the different models. I would like to know why i get different results in cross validation and what can be done about it

Answer 1

I found the solution to my question.

Setting a random seed with the below solved the problem:

seed = np.random.seed(22)