Machine Learning HeroChapter 81

Chapter 3: Data Preprocessing and Feature Engineering

Section 2 of 4-~ 12 min read-Synced from Cuantum content
  1. What is the purpose of data cleaning in data preprocessing?
  2. - a) To improve model performance by transforming features
  • b) To identify and handle missing data, remove duplicates, and correct errors
  • c) To scale data to a consistent range
  • d) To reduce the dimensionality of the dataset
  1. Which technique is typically used for handling missing data?
  2. - a) One-hot encoding
  • b) Data augmentation
  • c) Imputation
  • d) PCA
  1. Feature engineering involves which of the following?
  2. - a) Creating new features from existing ones
  • b) Reducing noise from the data
  • c) Increasing the number of samples in the dataset
  • d) Both a and b
  1. Why is it important to scale numerical features?
  2. - a) To remove outliers from the dataset
  • b) To ensure features with different ranges contribute equally to model performance
  • c) To increase the size of the dataset
  • d) To remove noise from the dataset
  1. What is the Train-Test Split used for?
  2. - a) Creating synthetic data samples
  • b) Separating data into training and testing sets for model validation
  • c) Increasing the number of features in the dataset
  • d) Standardizing features to the same scale