Definition: Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function.   

Detailed Description:

Regularization helps to avoid overfitting by discouraging the model from learning overly complex patterns in the training data. Common regularization techniques include:

• L1 regularization: Adds a penalty term proportional to the absolute value of the model’s weights. 
  
   
• L2 regularization: Adds a penalty term proportional to the square of the model’s weights.
• Dropout: Randomly drops units from the neural network during training to prevent overfitting.

Regularization can improve the generalization performance of a model, making it less likely to overfit the training data and perform poorly on new, unseen data.

Related Terms:

• Machine Learning
• Overfitting
• Underfitting
• Loss Function
• Neural Networks

FAQs:

1. What is the difference between L1 and L2 regularization?

   • L1 regularization tends to promote sparsity in the model, while L2 regularization tends to shrink the weights towards zero.

2. How does regularization help prevent overfitting?

   • Regularization adds a penalty term to the loss function, discouraging the model from learning complex patterns that may not generalize well to new data.

3. When should regularization be used?

   • Regularization is often used when there is a risk of overfitting, such as when the training data is small or noisy.