We are achieving a CV score of 0.867 F1, but we are unable to surpass a 0.74 score on the LB. It seems like our models are struggling to learn effectively, and the signal in the features appears to be very weak.
@Lotfi_Motfi team, we have been working on this dataset for a long time, but I am still uncertain about the results.
Well you guys have cracked "0.74" So what is the secret to get to 0.74 :)
For me I gave up on this tbf, no learning is happening whatsoever no matter how much feature engineering we do. Infact the FE makes the scores even worse. So I am impressed with people getting 0.74 and 0.75
Actually, @Koleshjr, feature engineering doesn't seem to work well with this data. We are focusing more on tuning our model parameters, that's all. We did try feature engineering, but it isn't contributing much.
Thank you , May the luckiest win haha
Clearly, this is going to be lottery.
Totaly agree with you , this is definitely going to be a lottery . @Koleshjr What have helped me to reach 0.74~ is GBDT with oversampling of the minority classes using resample from sklearn.utils . And also I have used the spectral features from the satelite images .
Thank you so much @Kouassi_Jr for sharing
a hundred percent
Thank u @koleshjr
Totaly agree with you @machine_learning
Thanks you so much,
Thanks you a lot @Kouassi_Jr for sharing
have you checked for data leakage? it seems you're resampling the minority classes during training, perhaps there are duplicate train/valid samples and that's why the CV is much higher?