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K-NN and Environment Management Tools Quiz

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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?

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. (C) Signup and view all the answers

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

All of the above (D) Signup and view all the answers

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

Both (a) and (b) are correct. (A) Signup and view all the answers

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

It is a lazy learning algorithm, meaning it does not build a model during the training phase. (A) Signup and view all the answers

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

When the dataset has a complex, non-linear relationship between the input and output variables. (A) Signup and view all the answers

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

The curse of dimensionality, where the distance between data points becomes less meaningful as the number of dimensions increases. (B) Signup and view all the answers