BigQuery ML を使用した ML モデルの作成: チャレンジラボ のレビュー

Pratama Maximillian Jonathan · 1年以上前にレビュー済み

Limacher David · 1年以上前にレビュー済み

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chau damien · 1年以上前にレビュー済み

Macedo Patricia · 1年以上前にレビュー済み

good

Natta Samrat · 1年以上前にレビュー済み

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JY Patrick · 1年以上前にレビュー済み

Mridha Srijan · 1年以上前にレビュー済み

Mridha Srijan · 1年以上前にレビュー済み

Orsolino Christian · 1年以上前にレビュー済み

suhadi Brian shahputera · 1年以上前にレビュー済み

I had to use my head a little bit and I got a good feel of how I can use sql(BigQuery) to evaluate ML models on a dataset. I see that it can get very complex very fast. Thanks for keeping it simple. The lab has a nice UI and just the right amount of complexitity to test the system.

최 웅비 · 1年以上前にレビュー済み

Takacs Kalman · 1年以上前にレビュー済み

I found Task 4 of this lab to be misleading and frustrating. The instructions explicitly state that I need to "predict the average trip duration for all trips from the busiest bike sharing station in 2019 (based on the number of trips per station in 2019)." Based on these instructions, I wrote queries to: Calculate the busiest station in 2019 based on trip count Use that station to make predictions with the subscriber_type model Filter for Single Trip subscribers and 2019 data Calculate the average predicted duration However, the verification system would only accept a query that used a hardcoded station name '21st & Speedway @PCL', which is never mentioned anywhere in the instructions. This is problematic because: The instructions specifically tell us to base our solution on 2019 trip counts There's no indication we should use a pre-defined station The verification system contradicts the written instructions Students waste time trying to debug technically correct solutions A better approach would be to either: a) Update the instructions to specify using '21st & Speedway @PCL' if that's the required solution b) Or modify the verification system to accept solutions that correctly calculate and use the actual busiest station from 2019 data This discrepancy between instructions and verification criteria creates unnecessary confusion and doesn't reflect real-world data analysis practices where we would actually want to calculate the busiest station rather than assuming it.

Khandelwal Anshul · 1年以上前にレビュー済み

Hobbs Boone · 1年以上前にレビュー済み