Hi I am fairly new to ML. I created training and validitation datasets and despite both of them scoring quite low in RMSE (around 4-7 range), they scored 10 and 11 RMSE in the public leaderboard. Are there any tips and tricks to prevent or reduce overfitting, or any indicators that quantifies how much your model has overfit?
Essayez de representer la courbe d'apprentissage pour voir un peu comment le modele se comporte et s'il faut realiser une validation croisée bien complexe , fait le
Could be due to overfitting
Hi I am fairly new to ML. I created training and validitation datasets and despite both of them scoring quite low in RMSE (around 4-7 range), they scored 10 and 11 RMSE in the public leaderboard. Are there any tips and tricks to prevent or reduce overfitting, or any indicators that quantifies how much your model has overfit?
Essayez de representer la courbe d'apprentissage pour voir un peu comment le modele se comporte et s'il faut realiser une validation croisée bien complexe , fait le
you can try cross validation to see how is your model generality