My Learning Cart

What is the purpose of using regularization techniques in model selection?

Practice Questions

Q1
What is the purpose of using regularization techniques in model selection?
  1. To increase the model's complexity
  2. To reduce the training time
  3. To prevent overfitting by penalizing large coefficients
  4. To improve the interpretability of the model

Questions & Step-by-Step Solutions

What is the purpose of using regularization techniques in model selection?
  • Step 1: Understand that when we create a model, we want it to learn from the data.
  • Step 2: Sometimes, a model can learn too much from the training data, including noise or random fluctuations. This is called overfitting.
  • Step 3: Overfitting means the model performs well on training data but poorly on new, unseen data.
  • Step 4: Regularization techniques help prevent overfitting by adding a penalty to the model for having large coefficients (weights).
  • Step 5: This penalty encourages the model to keep the coefficients smaller, making it simpler and more generalizable.
  • Step 6: By using regularization, we aim to improve the model's performance on new data.
No concepts available.
Soulshift Feedback ×

On a scale of 0–10, how likely are you to recommend The Soulshift Academy?

Not likely Very likely