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Which technique is used to reduce the complexity and size of a model while maintaining its predictive power?

  • Feature Engineering
  • Regularization
  • Clustering
  • Dimensionality Reduction
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What is the goal of feature extraction in data preprocessing?

  • To transform categorical features into numerical features.
  • To select the most relevant features for the model.
  • To create new features based on existing ones.
  • To handle missing values in the dataset.
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What is the main difference between supervised and unsupervised learning?

  • Supervised learning requires labeled data, while unsupervised learning does not.
  • Unsupervised learning requires labeled data, while supervised learning does not.
  • Supervised learning uses more complex algorithms than unsupervised learning.
  • Unsupervised learning is only used for classification tasks.
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What is the goal of hyperparameter tuning in machine learning?

  • To increase the complexity of the model.
  • To reduce the number of features in the dataset.
  • To optimize the model's performance by selecting the best hyperparameters.
  • To eliminate noise from the data.
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Which data visualization technique is used to show the distribution of a dataset along a single axis?

  • Scatter Plot
  • Histogram
  • Heatmap
  • Box Plot
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