I'm trying to classify a text to a 6 different classes.
Since I'm having an imbalanced dataset, I'm also using SMOTETomek method that should synthetically balance the dataset with additional artificial samples.

I've noticed a huge score difference when applying it via pipeline vs 'Step by step" where the only difference is (I believe) the place I'm using `train_test_split`

Here are my features and labels:

```
for curr_features, label in self.training_data:
    features.append(curr_features)
    labels.append(label)

algorithms = [
    linear_model.SGDClassifier(loss='hinge', penalty='l2', alpha=1e-3, random_state=42, max_iter=5, tol=None),
    naive_bayes.MultinomialNB(),
    naive_bayes.BernoulliNB(),
    tree.DecisionTreeClassifier(max_depth=1000),
    tree.ExtraTreeClassifier(),
    ensemble.ExtraTreesClassifier(),
    svm.LinearSVC(),
    neighbors.NearestCentroid(),
    ensemble.RandomForestClassifier(),
    linear_model.RidgeClassifier(),
]

```

**Using Pipeline:**

```
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# Provide Report for all algorithms
score_dict = {}
for algorithm in algorithms:
    model = Pipeline([
        ('vect', CountVectorizer()),
        ('tfidf', TfidfTransformer()),
        ('smote', SMOTETomek()),
        ('classifier', algorithm)
    ])
    model.fit(X_train, y_train)

    # Score
    score = model.score(X_test, y_test)
    score_dict[model] = int(score * 100)

sorted_score_dict = {k: v for k, v in sorted(score_dict.items(), key=lambda item: item[1])}
for classifier, score in sorted_score_dict.items():
    print(f'{classifier.__class__.__name__}: score is {score}%')

```

**Using Step by Step:**

```
vectorizer = CountVectorizer()
transformer = TfidfTransformer()
cv = vectorizer.fit_transform(features)
text_tf = transformer.fit_transform(cv).toarray()

smt = SMOTETomek()
X_smt, y_smt = smt.fit_resample(text_tf, labels)

X_train, X_test, y_train, y_test = train_test_split(X_smt, y_smt, test_size=0.2, random_state=0)
self.test_classifiers(X_train, X_test, y_train, y_test, algorithms)

def test_classifiers(self, X_train, X_test, y_train, y_test, classifiers_list):
    score_dict = {}
    for model in classifiers_list:
        model.fit(X_train, y_train)

        # Score
        score = model.score(X_test, y_test)
        score_dict[model] = int(score * 100)

    print()
    print("SCORE:")
    sorted_score_dict = {k: v for k, v in sorted(score_dict.items(), key=lambda item: item[1])}
    for model, score in sorted_score_dict.items():
        print(f'{model.__class__.__name__}: score is {score}%')

```

I'm getting (for the best classifier model) around 65% using pipeline vs 90% using step by step.
Not sure what am I missing.