What is a common challenge when selecting features for a model?
Practice Questions
Q1
What is a common challenge when selecting features for a model?
Overfitting due to too many features
Underfitting due to too few features
Both A and B
None of the above
Questions & Step-by-Step Solutions
What is a common challenge when selecting features for a model?
Step 1: Understand what features are. Features are the input variables used in a model to make predictions.
Step 2: Know that selecting too many features can lead to overfitting. This means the model learns the training data too well, including noise, and performs poorly on new data.
Step 3: Recognize that selecting too few features can lead to underfitting. This means the model is too simple and cannot capture the underlying patterns in the data.
Step 4: Realize that finding the right number of features is important for the model to perform well on both training and new data.
