As per the data description : \" 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\".
\nHence, should we drop \"PCIAT-PCIAT_Total\" from training data, as we need to predict only \"sii\" and test data also doesnt have \"PCIAT-PCIAT_Total\" feature ? Any suggestions, Please.
", "messages": [ { "id": "3017323", "postDate": "10/14/2024 18:54:44", "content": "As per the data description : \" 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\".
\nHence, should we drop \"PCIAT-PCIAT_Total\" from training data, as we need to predict only \"sii\" and test data also doesnt have \"PCIAT-PCIAT_Total\" feature ? Any suggestions, Please.
", "rawMarkdown": "As per the data description : \" 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\nHence, should we drop \"PCIAT-PCIAT_Total\" from training data, as we need to predict only \"sii\" and test data also doesnt have \"PCIAT-PCIAT_Total\" feature ? Any suggestions, Please.", "votes": null }, { "id": "3017328", "postDate": "10/14/2024 19:08:16", "content": "Obviously yes @aniruddhapa
", "rawMarkdown": "Obviously yes @aniruddhapa", "votes": null }, { "id": "3017341", "postDate": "10/14/2024 19:30:15", "content": "I've dropped all the PCIAT group fields
", "rawMarkdown": "I've dropped all the PCIAT group fields", "votes": null }, { "id": "3018048", "postDate": "10/15/2024 13:33:42", "content": "You can use \"PCIAT-PCIAT_Total\" as target, it will be a really regression problem. And transform the predicted \"PCIAT-PCIAT_Total\" into predicted \"sii\" finally.
", "rawMarkdown": "You can use \"PCIAT-PCIAT_Total\" as target, it will be a really regression problem. And transform the predicted \"PCIAT-PCIAT_Total\" into predicted \"sii\" finally.", "votes": null }, { "id": "3045808", "postDate": "11/14/2024 19:58:38", "content": "Thank you @ggggpeushmy for your suggestion. I will definately give it a try.
", "rawMarkdown": "Thank you @ggggpeushmy for your suggestion. I will definately give it a try.", "votes": null }, { "id": "3045809", "postDate": "11/14/2024 19:59:24", "content": "Thank you so much @ravi20076 for your response.
", "rawMarkdown": "Thank you so much @ravi20076 for your response.", "votes": null }, { "id": "3075386", "postDate": "12/18/2024 19:00:55", "content": "Totally agree with your suggestion, as predicting the test scores and then turning those scores to labels(0,1,2,3) using some thresholds mimic the way how those labels arose in the training data in the first place!
", "rawMarkdown": "Totally agree with your suggestion, as predicting the test scores and then turning those scores to labels(0,1,2,3) using some thresholds mimic the way how those labels arose in the training data in the first place!", "votes": null } ], "comments": [ { "id": 3017328, "author_name": "ravi20076", "author_url": "", "post_date": "10/14/2024 19:08:16", "content": "Obviously yes @aniruddhapa
", "votes": null, "replies": [ { "id": 3045809, "author_name": "aniruddhapa", "author_url": "", "post_date": "11/14/2024 19:59:24", "content": "Thank you so much @ravi20076 for your response.
", "votes": null, "replies": [] } ] }, { "id": 3017341, "author_name": "stuarthoughton", "author_url": "", "post_date": "10/14/2024 19:30:15", "content": "I've dropped all the PCIAT group fields
", "votes": null, "replies": [] }, { "id": 3018048, "author_name": "ggggpeushmy", "author_url": "", "post_date": "10/15/2024 13:33:42", "content": "You can use \"PCIAT-PCIAT_Total\" as target, it will be a really regression problem. And transform the predicted \"PCIAT-PCIAT_Total\" into predicted \"sii\" finally.
", "votes": null, "replies": [ { "id": 3045808, "author_name": "aniruddhapa", "author_url": "", "post_date": "11/14/2024 19:58:38", "content": "Thank you @ggggpeushmy for your suggestion. I will definately give it a try.
", "votes": null, "replies": [] }, { "id": 3075386, "author_name": "viktoriamelkumyan", "author_url": "", "post_date": "12/18/2024 19:00:55", "content": "Totally agree with your suggestion, as predicting the test scores and then turning those scores to labels(0,1,2,3) using some thresholds mimic the way how those labels arose in the training data in the first place!
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "3017323": "As per the data description : \" 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\nHence, should we drop \"PCIAT-PCIAT_Total\" from training data, as we need to predict only \"sii\" and test data also doesnt have \"PCIAT-PCIAT_Total\" feature ? Any suggestions, Please.", "3017328": "Obviously yes @aniruddhapa", "3017341": "I've dropped all the PCIAT group fields", "3018048": "You can use \"PCIAT-PCIAT_Total\" as target, it will be a really regression problem. And transform the predicted \"PCIAT-PCIAT_Total\" into predicted \"sii\" finally.", "3045808": "Thank you @ggggpeushmy for your suggestion. I will definately give it a try.", "3045809": "Thank you so much @ravi20076 for your response.", "3075386": "Totally agree with your suggestion, as predicting the test scores and then turning those scores to labels(0,1,2,3) using some thresholds mimic the way how those labels arose in the training data in the first place!" }, "source": "meta" }