In a case study, which method is often used to evaluate the effectiveness of feature engineering?
Practice Questions
1 question
Q1
In a case study, which method is often used to evaluate the effectiveness of feature engineering?
Cross-validation
Data normalization
Hyperparameter tuning
Model deployment
Cross-validation helps assess how well the feature engineering has improved model performance.
Questions & Step-by-step Solutions
1 item
Q
Q: In a case study, which method is often used to evaluate the effectiveness of feature engineering?
Solution: Cross-validation helps assess how well the feature engineering has improved model performance.
Steps: 5
Step 1: Understand what feature engineering is. It is the process of using domain knowledge to create features that make machine learning algorithms work better.
Step 2: Learn about model performance. This refers to how well a machine learning model makes predictions.
Step 3: Know what cross-validation is. It is a technique used to evaluate the performance of a model by splitting the data into parts, training the model on some parts, and testing it on others.
Step 4: Realize that cross-validation helps to see if the changes made during feature engineering actually improve the model's predictions.
Step 5: Use cross-validation to compare the model's performance before and after feature engineering to see if there is an improvement.
