Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
Over-fitting of Machine Learning model
#1
When a model performs well on training data but poorly on untried data, this is over-fitting. 

Over-fitting is a very common problem in Machine Learning. There is an extensive range of literature dedicated to studying methods for preventing over-fitting.
Three simple steps to avoid Over-fitting:
  • Train with more data - Training with more data can help algorithms detect the signal better (But, it won’t work every time).
  • Keep the model simple - take fewer variables into account, thereby removing some of the noise in the training data
  • Utilize cross-validation methods, such as the k-folds method.
  • Use regularization techniques - Regularization describes a wide range of methods for forcibly simplifying your model, such as LASSO, that penalize certain model parameters if they're likely to cause over-fitting.
  • Ensembling - Machine learning techniques called ensembles are used to combine predictions from various independent models.
Reply


Messages In This Thread
Over-fitting of Machine Learning model - by Abhiram.m - 12-23-2022, 07:10 AM

Forum Jump:


Users browsing this thread: 1 Guest(s)