code: def correlated_features(features, threshold=0.95):
      """Compute pairwise correlation of features using pearson method
  
      Parameters
      ----------
      features : DataFrame
          features
      threshold :
          correlation value for removing highly correlated features
      Returns
      -------
      DataFrame
          correlated features names
  
      """
      corr_matrix = features.corr().abs()
      # Select upper triangle of correlation matrix
      upper = corr_matrix.where(
          np.triu(np.ones(corr_matrix.shape), k=1).astype(bool)
      )
      # Find index and column name of features with correlation greater than 0.95
      to_drop = [
          column
          for column in upper.columns
          if any(upper[column] > threshold)
      ]
  
      return to_drop
  
imports: import numpy
  import pandas
  from scipy.interpolate import interp1d
label_desc: Use image node_id 3 for calling a global function with example usage: correlated_features(features, threshold) and returns: to_drop