• 首先，您不需要删除异常值，因为像 XGBoost 这样的“决策族算法”可以处理它。

• 其次，您可以使用 Tukey 方法 (Tukey JW., 1977)：

  def detect_outliers(df,n,features):
      outlier_indices = []
      # iterate over features(columns)
      for col in features:
          # 1st quartile (25%)
          Q1 = np.percentile(df[col], 25)
          # 3rd quartile (75%)
          Q3 = np.percentile(df[col],75)
          # Interquartile range (IQR)
          IQR = Q3 - Q1
          # outlier step
          outlier_step = 1.5 * IQR
          # Determine a list of indices of outliers for feature col
          outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step )].index
          # append the found outlier indices for col to the list of outlier indices 
          outlier_indices.extend(outlier_list_col)
          # select observations containing more than 2 outliers
          outlier_indices = Counter(outlier_indices)        
          multiple_outliers = list( k for k, v in outlier_indices.items() if v > n )
          return multiple_outliers 
  Outliers_to_drop = detect_outliers(data,2,["col1","col2"])
  data.loc[Outliers_to_drop] # Show the outliers rows
  # Drop outliers
  data= data.drop(Outliers_to_drop, axis = 0).reset_index(drop=True)
  

https://www.kaggle.com/yassineghouzam/titanic-top-4-with-ensemble-modeling

• 第三，我建议您尝试离散（分箱）连续变量，而不是删除 xgboost 的异常值。