input: Write a python function to classify text data using Naive Bayes and evaluate the model performance using classification report.
  ```python
  def classify_text_data(data, target, test_size=0.25, random_state=0):
      from sklearn.datasets import fetch_20newsgroups
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.model_selection import train_test_split
      from sklearn import metrics
  
      X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=test_size, random_state=random_state)
  
      vectorizer = TfidfVectorizer()
      X_train = vectorizer.fit_transform(X_train)
      X_test = vectorizer.transform(X_test)
  
      clf = MultinomialNB().fit(X_train, y_train)
      predicted = clf.predict(X_test)
      return metrics.classification_report(y_test, predicted)
  
  # Load the data
  dataset = fetch_20newsgroups(shuffle=True, random_state=1, subset='all')
  
  # Test cases
  assert "rec.autos" in classify_text_data(dataset.data, dataset.target)
  assert "sci.med" in classify_text_data(dataset.data, dataset.target)
  assert "talk.religion.misc" in classify_text_data(dataset.data, dataset.target)
output: def classify_text_data(data, target, test_size=0.25, random_state=0):
      from sklearn.datasets import fetch_20newsgroups
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.model_selection import train_test_split
      from sklearn import metrics
  
      X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=test_size, random_state=random_state)
  
      vectorizer = TfidfVectorizer()
      X_train = vectorizer.fit_transform(X_train)
      X_test = vectorizer.transform(X_test)
  
      clf = MultinomialNB().fit(X_train, y_train)
      predicted = clf.predict(X_test)
      return metrics.classification_report(y_test, predicted)
  
  # Load the data
  dataset = fetch_20newsgroups(shuffle=True, random_state=1, subset='all')
  
  # Test cases
  assert "rec.autos" in classify_text_data(dataset.data, dataset.target)
  assert "sci.med" in classify_text_data(dataset.data, dataset.target)
  assert "talk.religion.misc" in classify_text_data(dataset.data, dataset.target)