Why embarkation_point_2 field gets added when one_hot_encoder is applied to training data-CodePudding

Following the example of vertica at https://www.vertica.com/docs/11.0.x/HTML/Content/Authoring/AnalyzingData/MachineLearning/DataPreparation/EncodingCategoricalColumns.htm?tocpath=Analyzing Data|Machine Learning for Predictive Analytics|Data Preparation|_____3

which uses Titanic data from kaggle,

ONE_HOT_ENCODER_FIT function coverts categorical data and creates a model which represents the new representation of categorical data

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

Then on applying the model titanic_encoder like this on titanic_training data, why does embarkation_point_2 gets added? Should the output contain only the categorical value (say S) and its encoded value ? Why do I see values 0 and 1 and not 2 (which is the encoded value for S? Similar to sex M and sex_1 1

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)

Why there is no embarkation_point_3?

CodePudding user response：

There are many reasons to your output. First, read the documentation of the APPLY_ONE_HOT_ENCODER: https://www.vertica.com/docs/11.0.x/HTML/Content/Authoring/SQLReferenceManual/Functions/MachineLearning/APPLY_ONE_HOT_ENCODER.htm?tocpath=SQL Reference Manual|SQL Functions|Machine Learning Functions|Transformation Functions|_____5

Two parameters allow you to achieve your goals:

drop_first: set it to false to get all the columns. One is dropped because of correlations purposes. You can read this article: https://inmachineswetrust.com/posts/drop-first-columns/ There are pros and cons.
column_naming: set it to values but be careful. If you have categories with special characters, you might face some difficulties.

Badr