Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, how does the k value affects the model using kNN, when does knn is sensitive to outliers, choosing a high value for k is better in kNN algorithm?
Machine Learning MCQ - Effect of choosing a small k value in kNN clustering algorithm
1. In k-Nearest Neighbour (kNN clustering) algorithm,
choosing a small value for k will lead to
a) Low bias and high variance
b) Low
variance and high bias
c) Balanced
bias and variance
d) K value doesn’t do anything with
bias and variance
|
Answer: (a) Low bias and high variance Choosing a small value for k will make the model more
sensitive to individual data points in the training data. This means that the
algorithm can overfit (low bias – biased to the
training data, and high variance – highly variable predictions on test data)
the training data, producing a model that is very flexible and can capture
the finer details and noise in the data. Hence, the predictions will closely
match the training data, leading to low bias. Small k – model
is flexible hence low bias, model is highly
variable (prediction determined by a single data point) hence high variance.
What
will a large k value do to the model? More data points (large
k) are taken into account hence the noise can be reduced (outliers may not
affect the model) The model generalizes
well hence high bias, least affected by
individual data points hence low variamce.
Note: Data with more
outliers or noise will likely perform better with higher values of k. |
Related links:
Why choosing small k value in knn lead to better results?
What is the effect of choosing a small k value for knn?
Why choosing a small value for k will lead to low bias and high variance in knn algorithm?
Small or large k value, which is better for generalizing the model using knn algorithm?
Machine learning solved mcq, machine learning solved mcq
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