code: def f_score(X, y):
      """
      This function implements the anova f_value feature selection (existing method for classification in scikit-learn),
      where f_score = sum((ni/(c-1))*(mean_i - mean)^2)/((1/(n - c))*sum((ni-1)*std_i^2))
  
      Input
      -----
      X: {numpy array}, shape (n_samples, n_features)
          input data
      y : {numpy array},shape (n_samples,)
          input class labels
  
      Output
      ------
      F: {numpy array}, shape (n_features,)
          f-score for each feature
      """
  
      F, pval = f_classif(X, y)
      return F
  
label_desc: Use image node_id 1 for calling a global function with example usage: f_score(X, y) and returns: F