input: Write a python function to split the dataset into training and test sets, train a RandomForestRegressor model, and return the model's score.
  assert split_train_score(df) > 0.5
output: from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestRegressor
  
  def split_train_score(df):
      X = df.drop('target', axis=1)
      y = df['target']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      model = RandomForestRegressor(random_state=42)
      model.fit(X_train, y_train)
      
      return model.score(X_test, y_test)