Codelab regresi linear

1. Pengantar

Codelab ini akan mengajari Anda cara menggunakan regresi linear untuk membuat model yang memprediksi biaya per klik.

Prasyarat

Untuk menyelesaikan codelab ini, Anda memerlukan data kampanye berkualitas tinggi yang cukup untuk membuat model.

2. Membuat tabel sementara

Jalankan kueri berikut

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. Membuat dan melatih model

Praktik terbaiknya adalah memisahkan langkah-langkah pembuatan tabel dari langkah-langkah pembuatan model Anda.

Jalankan kueri berikut pada tabel sementara yang Anda buat di langkah sebelumnya. Jangan khawatir untuk memberikan tanggal mulai dan akhir, karena tanggal tersebut akan disimpulkan berdasarkan data dalam tabel sementara.

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`)

Baris	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