input: 
  def train_binary_classifier(data):
      """ Trains a basic machine learning model for binary classification on the given data.
      >>> train_binary_classifier([
      [0.3, 0.2, 0.6],
      [0.3, 0.1, 0.4],
      [0.2, 0.3, 0.4],
      [0.7, 0.4, 0.2],
      [0.5, 0.2, 0.1]
      ])
      'Model accuracy: 1.0'
      """
  
output:     
      from sklearn.linear_model import LogisticRegression
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      X = [d[:-1] for d in data]
      y = [d[-1] for d in data]
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = LogisticRegression()
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return f'Model accuracy: {accuracy}'
  