Codelab de regressão linear

1. Introdução

Este codelab ensina a usar a regressão linear para criar um modelo que prevê o custo por clique.

Pré-requisitos

Para concluir este codelab, tem de ter:

Para concluir este codelab, vai precisar de dados de campanhas de alta qualidade suficientes para criar um modelo.

2. Crie uma tabela temporária

Execute a seguinte consulta

CREATE TABLE
 linear_regression_example_data
AS(
 WITH all_data AS (
   SELECT
     imp.user_id as user_id,
     ROW_NUMBER() OVER(PARTITION BY imp.user_id) AS rowIdx,
     imp.browser AS browser_name,
     gender_name AS gender_name,
     age_group_name AS age_group_name,
     DATETIME(TIMESTAMP_MICROS(
       imp.query_id.time_usec), "America/Los_Angeles") as impression_time,
     clk.advertiser_click_cost_usd AS label
   FROM adh.google_ads_impressions imp
     INNER JOIN adh.google_ads_clicks clk USING (impression_id)
     LEFT JOIN adh.gender ON demographics.gender = gender_id
     LEFT JOIN adh.age_group ON demographics.age_group = age_group_id
 )
 # Need just one user ID or regression won't work
 SELECT
   label,
   browser_name,
   gender_name,
   age_group_name,
   # Although BQML could divide impression_time into several useful variables on
   # its own, it may attempt to divide it into too many features. As a best
   # practice extract the variables that you think will be most helpful.
   # The output of impression_time is a number, but we care about it as a
   # category, so we cast it to a string.
   CAST(EXTRACT(DAYOFWEEK FROM impression_time) AS STRING) AS day_of_week,
   CAST(EXTRACT(HOUR FROM impression_time) AS STRING) AS hour,
 FROM
   all_data
 WHERE
   rowIdx = 1 # This ensures that there's only 1 row per user.
   AND
   label IS NOT NULL
   AND
   gender_name IS NOT NULL
   AND
   age_group_name IS NOT NULL
);

3. Crie e prepare um modelo

É uma prática recomendada separar os passos de criação da tabela dos passos de criação do modelo.

Execute a seguinte consulta na tabela temporária que criou no passo anterior. Não se preocupe em fornecer datas de início e fim, uma vez que estas serão inferidas com base nos dados da tabela temporária.

CREATE OR REPLACE
MODEL `example_linear`
OPTIONS(
   model_type = 'adh_linear_regression'
)
AS (
   SELECT *
   FROM
       tmp.linear_regression_example_data
);

SELECT * FROM ML.EVALUATE(MODEL `example_linear`)

Linha	mean_absolute_error	mean_squared_error	mean_squared_log_error	median_absolute_error	r2_score	explained_variance
1	0,11102380666874107	0,019938972461569476	0,019503393448234131	0,091792024503562136	-9,8205955364568478	-9,7975398794423025