K-NN and Environment Management Tools Quiz
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Questions and Answers

Which of the following statements about K-Nearest Neighbors (K-NN) is true?

  • K-NN is a non-parametric learning algorithm that does not make any assumptions about the underlying data distribution. (correct)
  • K-NN keeps all the data in memory during the training and prediction phases.
  • K-NN builds an equation to model the relationship between input and output variables.
  • K-NN is a parametric supervised learning algorithm.

    • In the K-NN algorithm, what does the 'K' represent?

  • The number of nearest neighbors to consider for prediction. (correct)
  • The number of classes in the target variable.
  • The number of iterations required for training the algorithm.
  • The number of features in the input data.

    • How does the K-NN algorithm make predictions for a new data point?

  • It fits a linear regression model to the training data and uses the model equation to make a prediction.
  • It builds a decision tree based on the training data and traverses the tree to make a prediction.
  • It calculates the Euclidean distance between the new data point and all the training data points, and selects the 'K' nearest neighbors. (correct)
  • It clusters the training data into 'K' groups and assigns the new data point to the closest cluster.

    • What is a potential disadvantage of the K-NN algorithm?

    <p>It is computationally expensive, especially for large datasets, as it needs to calculate the distances between the new data point and all the training data points.</p> Signup and view all the answers

    Which of the following distance metrics can be used in the K-NN algorithm?

    <p>All of the above</p> Signup and view all the answers

    How does the value of 'K' affect the performance of the K-NN algorithm?

    <p>Both (a) and (b) are correct.</p> Signup and view all the answers

    Which of the following statements about the K-NN algorithm is true?

    <p>It is a lazy learning algorithm, meaning it does not build a model during the training phase.</p> Signup and view all the answers

    In which of the following scenarios would the K-NN algorithm be a good choice?

    <p>When the dataset has a complex, non-linear relationship between the input and output variables.</p> Signup and view all the answers

    What is a potential issue with the K-NN algorithm when dealing with high-dimensional data?

    <p>The curse of dimensionality, where the distance between data points becomes less meaningful as the number of dimensions increases.</p> Signup and view all the answers

    Which of the following techniques can be used to improve the performance of the K-NN algorithm?

    <p>All of the above.</p> Signup and view all the answers

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