使用 KNN、SoftImpute 进行数据插补

Question

我想使用 MICE、KNN 和 Soft Impute 对来自 fancyimpute 包的插补值进行比较，但是，当我运行我的代码时，与更合理的插补值相比，KNN 和 SoftImpute 只为我的值插补了 0由 MICE 提供。

imputed_numerical=train[['Age']].select_dtypes(include='number']).as_matrix()

Age_MICE=MICE().complete(imputed_numerical)
Age_KNN=KNN(k=3).complete(imputed_numerical)
Age_SoftImpute=SoftImpute().complete(imputed_numerical)

我将结果放入如下所示的数据框中：

Not_Imputed MICE    KNN SoftImpute
   22.0    [22.0]  [22.0]  [22.0]
   38.0    [38.0]  [38.0]  [38.0]
   26.0    [26.0]  [26.0]  [26.0]
   35.0    [35.0]  [35.0]  [35.0]
   35.0    [35.0]  [35.0]  [35.0]
   NaN     [29]    [0.0]   [0.0]
   54.0    [54.0]  [54.0]  [54.0]
   2.0     [2.0]   [2.0]   [2.0]
   27.0    [27.0]  [27.0]  [27.0]
   14.0    [14.0]  [14.0]  [14.0]
   4.0     [4.0]   [4.0]   [4.0]
   58.0    [58.0]  [58.0]  [58.0]
   20.0    [20.0]  [20.0]  [20.0]
   39.0    [39.0]  [39.0]  [39.0]
   14.0    [14.0]  [14.0]  [14.0]
   55.0    [55.0]  [55.0]  [55.0]
   2.0     [2.0]   [2.0]   [2.0]
   NaN     [27.6]  [0.0]   [0.0]
   31.0    [31.0]  [31.0]  [31.0]
   NaN     [30]    [0.0]   [0.0]

问题：为什么 KNN 和 SoftImpute 只将 0 归为完成值？

Answer 1

问题是这些是多变量过程，但您只使用一个变量（列）。 MICE 执行多元回归，KNN 取 N 个邻居的平均值，它们最接近多维空间中的缺失值（每个维度都是一个变量），我不确定 softImpute 但它也可能是一个多元过程.

例如，查看来自 knn 过程的警告消息：

[KNN] Warning: 3/20 still missing after imputation, replacing with 0

或来自 SoftImpute 的警告：

RuntimeWarning: invalid value encountered in double_scalars
  return (np.sqrt(ssd) / old_norm) < self.convergence_threshold