为什么在将one_hot_encoder应用于训练数据时会添加departation_point_2字段

Question

以vertica为例https://www.vertica.com/docs/11.0.x/HTML/Content/Authoring/AnalyzingData/MachineLearning/DataPreparation/EncodingCategoricalColumns.htm?tocpath=Analyzing%20Data%7CMachine%20Learning%20for%20Predictive%20Analytics%7CData%20Preparation%7C_____3

它使用来自kaggle、的泰坦尼克号数据

ONE_HOT_ENCODER_FIT函数覆盖分类数据并创建一个表示分类数据的新表示的模型

SELECT one_hot_encoder_fit('public.titanic_encoder','titanic_training','sex, embarkation_point'  USING PARAMETERS exclude_columns='', output_view='', extra_levels='{}');
==================
varchar_categories
==================
category_name  |category_level|category_level_index
-----------------+--------------+--------------------
embarkation_point|      C       |         0
embarkation_point|      Q       |         1
embarkation_point|      S       |         2 <- note S is 2
embarkation_point|              |         3
sex       |    female    |         0
sex       |     male     |         1 <-- note male is 1

那么，在titanic_training数据上应用这样的模型titanic_encoder时，为什么要添加embarkation_point_2？输出应该只包含分类值(比如S(及其编码值吗？为什么我看到值0和1而不是2(S的编码值是什么？类似于sexM和sex_11

dbadmin@2e4e746b3e6c(*)=> select * from titanic_training limit 1;
passenger_id | survived | pclass |          name           | sex  | age | sibling_and_spouse_count | parent_and_child_count |  ticket   | fare | cabin | embarkation_point
--------------+----------+--------+-------------------------+------+-----+--------------------------+------------------------+-----------+------+-------+-------------------
1 |        0 |      3 | Braund, Mr. Owen Harris | male |  22 |                        1 |                      0 | A/5 21171 | 7.25 |       | S <-- note S
(1 row)

dbadmin@2e4e746b3e6c(*)=> SELECT APPLY_ONE_HOT_ENCODER(* USING PARAMETERS model_name='titanic_encoder') from titanic_training limit 1;
passenger_id | survived | pclass |          name           | sex  | sex_1 | age | sibling_and_spouse_count | parent_and_child_count |  ticket   | fare | cabin | embarkation_point | embarkation_point_1 | embarkation_point_2 (<-- why this is here)?
--------------+----------+--------+-------------------------+------+-------+-----+--------------------------+------------------------+-----------+------+-------+-------------------+---------------------+---------------------
1 |        0 |      3 | Braund, Mr. Owen Harris | male <- note male|     1 <- note  encoded value of male |  22 |                        1 |                      0 | A/5 21171 | 7.25 |       | S <- note S                 |                   0 <- why this is here |                   1 <-- why this is here. Where is 2?
(1 row)

为什么没有embarkation_point_3？

Answer 1

您的输出有很多原因。首先，阅读APPLY_ONE_HOT_ENCODER的文档：https://www.vertica.com/docs/11.0.x/HTML/Content/Authoring/SQLReferenceManual/Functions/MachineLearning/APPLY_ONE_HOT_ENCODER.htm?tocpath=SQL%20Reference%20Manual%7CSQL%20Functions%7CMachine%20Learning%20Functions%7CTransformation%20Functions%7C_____5

有两个参数可以让你实现目标：

• drop_first：设置为false以获取所有列。其中一个是因为相关性的目的而被删除的。你可以阅读这篇文章：https://inmachineswetrust.com/posts/drop-first-columns/有优点也有缺点
• column_name：将其设置为值，但要小心。如果你有特殊字符的类别，你可能会遇到一些困难

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