What is the purpose of regularization in supervised learning?
Practice Questions
Q1
What is the purpose of regularization in supervised learning?
To increase the complexity of the model
To prevent overfitting
To improve training speed
To enhance feature selection
Questions & Step-by-Step Solutions
What is the purpose of regularization in supervised learning?
Step 1: Understand that supervised learning is a type of machine learning where we train a model using labeled data.
Step 2: Know that overfitting happens when a model learns the training data too well, including noise and outliers, making it perform poorly on new data.
Step 3: Realize that regularization is a technique used to prevent overfitting.
Step 4: Learn that regularization works by adding a penalty to the model for having large coefficients (weights) for features.
Step 5: Understand that this penalty encourages the model to keep the coefficients smaller, leading to a simpler model that generalizes better to new data.
