Multiple Choice Questions on Feature Engineering for AI

📚 Quick Study Guide

• ✨ Definition: Feature engineering is the process of selecting, manipulating, and transforming raw data into features that can be used in machine learning models. It aims to improve model accuracy and performance.
• 🔢 Feature Scaling: Techniques like Min-Max Scaling and Standardization transform features to a similar scale.
  • Min-Max Scaling: Scales values to a range between 0 and 1 using the formula: $x' = \frac{x - x_{min}}{x_{max} - x_{min}}$
  • Standardization: Scales values to have a mean of 0 and a standard deviation of 1 using the formula: $x' = \frac{x - \mu}{\sigma}$
• 📊 Feature Encoding: Converts categorical variables into numerical representations.
  • One-Hot Encoding: Creates binary columns for each category.
  • Label Encoding: Assigns a unique integer to each category.
• ➕ Feature Creation: Involves creating new features from existing ones. Examples include creating interaction features (e.g., multiplying two features) or polynomial features.
• 🗑️ Feature Selection: Reduces the number of features to improve model performance and reduce overfitting. Common methods include:
  • Univariate Feature Selection: Selects features based on statistical tests.
  • Recursive Feature Elimination: Recursively removes features and builds a model.
• 💡 Tips: Always understand your data first! Visualize your data, handle missing values, and iteratively refine your features based on model performance.

🧪 Practice Quiz

1. Which of the following is the primary goal of feature engineering?
   1. A. To reduce the size of the dataset
   2. B. To improve the accuracy and performance of machine learning models
   3. C. To simplify the model training process
   4. D. To make the data more visually appealing
2. What is the purpose of feature scaling?
   1. A. To convert categorical variables to numerical ones
   2. B. To transform features to a similar scale
   3. C. To remove irrelevant features
   4. D. To create new features from existing ones
3. Which of the following is an example of feature encoding?
   1. A. Standardization
   2. B. Min-Max Scaling
   3. C. One-Hot Encoding
   4. D. Feature Selection
4. What does the formula $x' = \frac{x - x_{min}}{x_{max} - x_{min}}$ represent?
   1. A. Standardization
   2. B. Z-score normalization
   3. C. Min-Max Scaling
   4. D. Feature Hashing
5. What is the purpose of feature selection?
   1. A. To increase the number of features
   2. B. To reduce the number of features and improve model performance
   3. C. To convert numerical features to categorical ones
   4. D. To visualize the data
6. Which of the following is a method of feature selection?
   1. A. One-Hot Encoding
   2. B. Recursive Feature Elimination
   3. C. Feature Scaling
   4. D. Label Encoding
7. What is feature creation?
   1. A. Reducing the number of features
   2. B. Converting categorical data to numerical data
   3. C. Creating new features from existing ones
   4. D. Scaling the features