Comments (4)
amirahff
commented on July 19, 2024
1
kalau colorCode=colorCode/1000 feature importancenya (menurut random forest) jadi tambah jelek.
entah ya, mgkn feature importancenya random forest semakin bervariasi semakin penting (gatau, blm tak cek iya po nggak)
atau mgkn harus dibinarize dulu setelahnya? (belum dicoba)
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luthfianto
commented on July 19, 2024
atau mgkn harus dibinarize dulu setelahnya? (belum dicoba)
bisa jadi. atau nggak di-binarize, tapi dikombinasi dengan articleID .groupby(['articleID','colorCode/1000']), terus dicari probability-nya
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luthfianto
commented on July 19, 2024
Kalau kata @amirahff , nggak mungkin RAL karena ada warna nomor 8800 padahal RAL cuma 8000-8029. Jadi sekarang ini cukup pakai colorCode_prob
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luthfianto
commented on July 19, 2024
Closed for awhile due to another high priority issue.
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Related Issues (20)
- Papan Mading HOT 5
- voucherID: Missing value HOT 1
- rrp: missing values HOT 4
- n_estimators HOT 2
- selidiki PolynomialFeatures HOT 5
- Tambahan feature extraction? HOT 1
- mean, variance, skewness, kurtosis HOT 1
- TPOT & for-in model2
- Ekstraksi Fitur HOT 2
- Seleksi Fitur Polynomial
- Return Probabilities HOT 1
- Transformasi Fitur
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- Payment Method: Binarize atau probability? HOT 2
- productGroup: simpan, buang, probability? atau... Impute!!!! HOT 5
- average_article_price: harga rerata item, dan apakah harga item lebih rendah/tinggi dari biasanya HOT 1
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