Codelab für die lineare Regression

1. Einleitung

In diesem Codelab erfahren Sie, wie Sie mithilfe der linearen Regression ein Modell zur Vorhersage des Cost-per-Click erstellen.

Voraussetzungen

Für dieses Codelab benötigen Sie ausreichend hochwertige Kampagnendaten zum Erstellen eines Modells.

2. Vorläufige Tabelle erstellen

Führen Sie die folgende Abfrage aus:

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. Modell erstellen und trainieren

Es wird empfohlen, die Schritte zur Tabellenerstellung von denen zur Modellerstellung zu trennen.

Führen Sie die folgende Abfrage für die temporäre Tabelle aus, die Sie im vorherigen Schritt erstellt haben. Sie müssen kein Start- und Enddatum angeben, denn diese werden aus den Daten in der temporären Tabelle abgeleitet.

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`)

Zeile	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