{ "id": 537328, "title": "PCIAT,instead of Sii", "url": "/competitions/child-mind-institute-problematic-internet-use/discussion/537328", "author_name": "", "post_date": "2024-10-02T17:00:20.530336600Z", "votes": 7, "comment_count": 4, "views": 0, "content": "

I used to predict the sii score as a regression task , but i find this : every sample have both sii and PCIAT,or don't have these two feature at the same time.

\n

and , I draw a pic for these two feature.. \"\"
\nI wish it could be helpful.

", "messages": [ { "id": "3005208", "postDate": "10/02/2024 17:00:20", "content": "

I used to predict the sii score as a regression task , but i find this : every sample have both sii and PCIAT,or don't have these two feature at the same time.

\n

and , I draw a pic for these two feature.. \"\"
\nI wish it could be helpful.

", "rawMarkdown": "I used to predict the sii score as a regression task , but i find this : every sample have both sii and PCIAT,or don't have these two feature at the same time.\n\nand , I draw a pic for these two feature.. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F15189707%2Fec04252fb2d7b22058e576bd4d030450%2Fresult%20(1).png?generation=1727888415601733&alt=media)\nI wish it could be helpful.", "votes": null }, { "id": "3005514", "postDate": "10/03/2024 03:09:20", "content": "

From the data description tab:

\n
\n

Note in particular the field PCIAT-PCIAT_Total. The target sii for this competition is derived from this field as described in the data dictionary: 0 for None, 1 for Mild, 2 for Moderate, and 3 for Severe.

\n
\n

and in the data_dict you can find the mapping between the two:

\n

Severity Impairment Index: 0-30 in PCIAT-PCIAT_Total=None; 31-49 =Mild; 50-79 =Moderate; 80-100=Severe

", "rawMarkdown": "From the data description tab:\n\n>Note in particular the field PCIAT-PCIAT_Total. The target sii for this competition is derived from this field as described in the data dictionary: 0 for None, 1 for Mild, 2 for Moderate, and 3 for Severe.\n\nand in the data_dict you can find the mapping between the two:\n\nSeverity Impairment Index: 0-30 in PCIAT-PCIAT_Total=None; 31-49 =Mild; 50-79 =Moderate; 80-100=Severe", "votes": null }, { "id": "3005587", "postDate": "10/03/2024 05:38:41", "content": "

..Thanks,I didnt find it at all before you told me..😭

", "rawMarkdown": "..Thanks,I didnt find it at all before you told me..😭", "votes": null }, { "id": "3006041", "postDate": "10/03/2024 16:50:46", "content": "

Thanks. Very interesting!

", "rawMarkdown": "Thanks. Very interesting!", "votes": null }, { "id": "3006690", "postDate": "10/04/2024 12:34:12", "content": "

@antoninadolgorukova I initially considered imputing the missing sii values using PCIAT-Total, but I realized that when sii is NaN, PCIAT-Total is also null, making that approach ineffective. If you’ve developed a method for imputing the sii values, I would greatly appreciate your guidance.

", "rawMarkdown": "antoninadolgorukova I initially considered imputing the missing sii values using PCIAT-Total, but I realized that when sii is NaN, PCIAT-Total is also null, making that approach ineffective. If you’ve developed a method for imputing the sii values, I would greatly appreciate your guidance.", "votes": null } ], "comments": [ { "id": 3005514, "author_name": "antoninadolgorukova", "author_url": "", "post_date": "10/03/2024 03:09:20", "content": "

From the data description tab:

\n
\n

Note in particular the field PCIAT-PCIAT_Total. The target sii for this competition is derived from this field as described in the data dictionary: 0 for None, 1 for Mild, 2 for Moderate, and 3 for Severe.

\n
\n

and in the data_dict you can find the mapping between the two:

\n

Severity Impairment Index: 0-30 in PCIAT-PCIAT_Total=None; 31-49 =Mild; 50-79 =Moderate; 80-100=Severe

", "votes": null, "replies": [ { "id": 3005587, "author_name": "yashi003", "author_url": "", "post_date": "10/03/2024 05:38:41", "content": "

..Thanks,I didnt find it at all before you told me..😭

", "votes": null, "replies": [] }, { "id": 3006690, "author_name": "nassimsfaxi", "author_url": "", "post_date": "10/04/2024 12:34:12", "content": "

@antoninadolgorukova I initially considered imputing the missing sii values using PCIAT-Total, but I realized that when sii is NaN, PCIAT-Total is also null, making that approach ineffective. If you’ve developed a method for imputing the sii values, I would greatly appreciate your guidance.

", "votes": null, "replies": [] } ] }, { "id": 3006041, "author_name": "nobodybutme", "author_url": "", "post_date": "10/03/2024 16:50:46", "content": "

Thanks. Very interesting!

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "3005208": "I used to predict the sii score as a regression task , but i find this : every sample have both sii and PCIAT,or don't have these two feature at the same time.\n\nand , I draw a pic for these two feature.. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F15189707%2Fec04252fb2d7b22058e576bd4d030450%2Fresult%20(1).png?generation=1727888415601733&alt=media)\nI wish it could be helpful.", "3005514": "From the data description tab:\n\n>Note in particular the field PCIAT-PCIAT_Total. The target sii for this competition is derived from this field as described in the data dictionary: 0 for None, 1 for Mild, 2 for Moderate, and 3 for Severe.\n\nand in the data_dict you can find the mapping between the two:\n\nSeverity Impairment Index: 0-30 in PCIAT-PCIAT_Total=None; 31-49 =Mild; 50-79 =Moderate; 80-100=Severe", "3005587": "..Thanks,I didnt find it at all before you told me..😭", "3006041": "Thanks. Very interesting!", "3006690": "antoninadolgorukova I initially considered imputing the missing sii values using PCIAT-Total, but I realized that when sii is NaN, PCIAT-Total is also null, making that approach ineffective. If you’ve developed a method for imputing the sii values, I would greatly appreciate your guidance." }, "source": "meta" }