Opinie (Create ML Models with BigQuery ML: Challenge Lab)

The lab doesn't work properly

Zuzanna M. · Sprawdzono ponad rok temu

Kevin C. · Sprawdzono ponad rok temu

I don't understand the task descriptions??? sometimes it says linear and sometimes logistic, I don't know what is the purpose of the task to be done

Zuzanna M. · Sprawdzono ponad rok temu

Christian A. · Sprawdzono ponad rok temu

Patrick L. · Sprawdzono ponad rok temu

Halimathu S. · Sprawdzono ponad rok temu

Bhagyashree P. · Sprawdzono ponad rok temu

Aman N. · Sprawdzono ponad rok temu

Camille H. · Sprawdzono ponad rok temu

Zhenru C. · Sprawdzono ponad rok temu

Zhenru C. · Sprawdzono ponad rok temu

Жандос О. · Sprawdzono ponad rok temu

Octavia R. · Sprawdzono ponad rok temu

Task 3 doesn't work

Pierpaolo S. · Sprawdzono ponad rok temu

Md I. · Sprawdzono ponad rok temu

Ozana I. · Sprawdzono ponad rok temu

I finished Task 3 by creating and evaluating the new model improved_customer_classification_model with the required additional features, but it would never mark it as complete. Here's the query I used to create the model: CREATE OR REPLACE MODEL `ecommerce.improved_customer_classification_model` OPTIONS ( model_type='logistic_reg', labels = ['will_buy_on_return_visit'] ) AS #standardSQL SELECT * EXCEPT(fullVisitorId) FROM # features (SELECT fullVisitorId, IFNULL(totals.bounces, 0) AS bounces, IFNULL(totals.timeOnSite, 0) AS time_on_site, IFNULL(totals.pageviews, 0) AS pageviews, trafficSource.source, trafficSource.medium, channelGrouping, # mobile or desktop device.deviceCategory, # geographic IFNULL(geoNetwork.country, "") AS country FROM `data-to-insights.ecommerce.web_analytics` WHERE totals.newVisits = 1 AND date BETWEEN '20160801' AND '20170430') # train on first 9 months JOIN (SELECT fullvisitorid, IF(COUNTIF(totals.transactions > 0 AND totals.newVisits IS NULL) > 0, 1, 0) AS will_buy_on_return_visit FROM `data-to-insights.ecommerce.web_analytics` GROUP BY fullvisitorid) USING (fullVisitorId) ; And here's how I evaluated it: SELECT roc_auc, CASE WHEN roc_auc > .9 THEN 'good' WHEN roc_auc > .8 THEN 'fair' WHEN roc_auc > .7 THEN 'decent' WHEN roc_auc > .6 THEN 'not great' ELSE 'poor' END AS model_quality FROM ML.EVALUATE(MODEL ecommerce.improved_customer_classification_model, ( #standardSQL SELECT * EXCEPT(fullVisitorId) FROM # features (SELECT fullVisitorId, IFNULL(totals.bounces, 0) AS bounces, IFNULL(totals.timeOnSite, 0) AS time_on_site, IFNULL(totals.pageviews, 0) AS pageviews, trafficSource.source, trafficSource.medium, channelGrouping, # mobile or desktop device.deviceCategory, # geographic IFNULL(geoNetwork.country, "") AS country FROM `data-to-insights.ecommerce.web_analytics` WHERE totals.newVisits = 1 AND date BETWEEN '20160801' AND '20170430') # train on first 9 months JOIN (SELECT fullvisitorid, IF(COUNTIF(totals.transactions > 0 AND totals.newVisits IS NULL) > 0, 1, 0) AS will_buy_on_return_visit FROM `data-to-insights.ecommerce.web_analytics` GROUP BY fullvisitorid) USING (fullVisitorId) ));

Adam W. · Sprawdzono ponad rok temu

Kevin C. · Sprawdzono ponad rok temu

Juan Carlos B. · Sprawdzono ponad rok temu

Manish S. · Sprawdzono ponad rok temu

Jesús Arnulfo C. · Sprawdzono ponad rok temu

Ilyas N. · Sprawdzono ponad rok temu

William B. · Sprawdzono ponad rok temu

Shannon M. · Sprawdzono ponad rok temu

Shannon M. · Sprawdzono ponad rok temu