Which of the following techniques can be used to address overfitting in linear r
Practice Questions
Q1
Which of the following techniques can be used to address overfitting in linear regression?
Increasing the number of features
Using regularization techniques like Lasso or Ridge
Decreasing the size of the training dataset
Ignoring outliers
Questions & Step-by-Step Solutions
Which of the following techniques can be used to address overfitting in linear regression?
Step 1: Understand what overfitting means. Overfitting happens when a model learns the training data too well, including noise, and performs poorly on new data.
Step 2: Learn about regularization. Regularization is a technique used to prevent overfitting by adding a penalty to the model for being too complex.
Step 3: Identify Lasso and Ridge as two types of regularization techniques. Lasso adds a penalty based on the absolute value of coefficients, while Ridge adds a penalty based on the square of coefficients.
Step 4: Apply Lasso or Ridge in your linear regression model to help reduce overfitting. This will make the model simpler and improve its performance on new data.
