Quizzes: k-nearest neighbors (KNN)

What is the main idea behind the k-nearest neighbors (KNN) algorithm?
a) It finds the linear relationship between variables.
b) It uses the k most similar training instances to predict the outcome of a new instance.
c) It reduces the dimensionality of data.
d) It clusters the data into k groups.

In the context of KNN, what does the parameter ‘k’ represent?
a) The number of features in the dataset.
b) The number of neighbors to consider when making a prediction.
c) The number of output classes.
d) The depth of the decision tree.

Why is it important to normalize or standardize data before applying KNN?
a) To improve the performance of the algorithm.
b) To ensure that all features contribute equally to the distance calculation.
c) To remove irrelevant features.
d) To increase the size of the dataset.

Which type of data is KNN most suitable for?
a) Categorical data
b) Numerical data
c) Text data
d) Image data

Which factor can significantly impact the performance of KNN?
a) The choice of distance metric
b) The value of ‘k’
c) The presence of outliers
d) All of the above

What is a potential drawback of using KNN on a very large dataset?
a) KNN is a parametric method and might overfit.
b) KNN requires a lot of memory and computation time for large datasets.
c) KNN cannot handle high-dimensional data.
d) KNN does not work well with binary classification.

How can the computational cost of KNN be reduced for large datasets?
a) By reducing the number of features.
b) By using approximate nearest neighbor algorithms.
c) By parallelizing the distance calculations.
d) All of the above

In which scenario would KNN be an inappropriate choice for a model?
a) When the dataset is very large.
b) When the dataset has many irrelevant features.
c) When the dataset is highly imbalanced.
d) All of the above