Data Science MCQs with answers Page - 134

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Q. What does the term "imputation" refer to in data preprocessing?

  • (A) Creating new features
  • (B) Filling in missing values in a dataset
  • (C) Encoding categorical data
  • (D) Creating new features

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Q. In machine learning, what is the term for the process of selecting the most important features for a model?

  • (A) Data preprocessing
  • (B) Model evaluation
  • (C) Feature selection
  • (D) Hyperparameter tuning

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Q. What is the primary purpose of the "Confusion Matrix" in the evaluation of classification models?

  • (A) To visualize data
  • (B) To build predictive models
  • (C) To summarize data
  • (D) To display the counts of true positive, true negative, false positive, and false negative predictions

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Q. In data science, what is the primary goal of dimensionality reduction techniques like Principal Component Analysis (PCA)?

  • (A) To reduce the number of features while preserving important information
  • (B) To add more features to the dataset
  • (C) To normalize data
  • (D) To visualize data

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Q. What does the acronym "ETL" stand for in the context of data warehousing?

  • (A) Encode, Tokenize, Leverage
  • (B) Estimate, Test, Label
  • (C) Explore, Train, Learn
  • (D) Extract, Transform, Load

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Q. In the context of natural language processing (NLP), what is the term for assigning parts of speech (e.g., noun, verb) to words in a text?

  • (A) Text vectorization
  • (B) Feature scaling
  • (C) Part-of-speech tagging
  • (D) One-hot encoding

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Q. What statistical technique is used to estimate a population parameter by repeatedly sampling from the population?

  • (A) Exploratory data analysis
  • (B) Bootstrap resampling
  • (C) Data preprocessing
  • (D) Exploratory data analysis

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Q. What is the main purpose of the K-nearest neighbors (K-NN) algorithm in machine learning?

  • (A) To perform regression analysis
  • (B) To cluster data into groups
  • (C) To reduce dimensionality
  • (D) To classify data points based on the majority class among their K nearest neighbors

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Q. What is the primary purpose of feature scaling in machine learning?

  • (A) To increase model complexity
  • (B) To add more features to the dataset
  • (C) To bring all features to a similar scale for better model performance
  • (D) To remove irrelevant features

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Q. What is the term for the process of converting text data into numerical form for machine learning?

  • (A) Feature scaling
  • (B) One-hot encoding
  • (C) Data normalization
  • (D) Text vectorization

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