10 Questions
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.
In the K-NN algorithm, what does the 'K' represent?
The number of nearest neighbors to consider for prediction.
How does the K-NN algorithm make predictions for a new data point?
It calculates the Euclidean distance between the new data point and all the training data points, and selects the 'K' nearest neighbors.
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.
Which of the following distance metrics can be used in the K-NN algorithm?
All of the above
How does the value of 'K' affect the performance of the K-NN algorithm?
Both (a) and (b) are correct.
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.
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.
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.
Which of the following techniques can be used to improve the performance of the K-NN algorithm?
All of the above.
This quiz covers topics such as K-Nearest Neighbors (K-NN) algorithm, KD-trees, Ball-trees, time complexity for prediction, Scikit-learn library, Conda for environment management, and Google Colab for online Jupyter notebooks. Test your knowledge on these topics and see how well you understand them!
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