Most Frequently Asked Interview Questions on KNN Algorithm
This article was published as a part of the Data Science Blogathon.
KNN stands for K-Nearest Neighbors, the supervised machine learning algorithm that can operate with both classification and regression tasks. KNN is often one of the hot topics in the interviews when the panel is interested in someone who can deal with sparse dataset situations or even a curse of dimensionality (we will discuss these things too) while working with the KNN model as creating a model is not a tedious task but dealing with its drawback and coming back with a solution is!
In this article, we will discuss some of the most asked and tricky questions so that one can answer not only specific questions but also cross-questions from the panel member. So let’s get started!
KNN Interview Questions
1. In what scenario KNN algorithm is required?
Suppose one is choosing KNN as their primary model. In that case, one needs to have sufficient domain knowledge of the problem statement he/she is working on, as the KNN algorithm can give us a high-accuracy model, but the same is not human-readable. Other than that, KNN can work accurately for classification problems where we need to find the data point (say X1) from two categories in the sample space.
Along with classification problems, KNN also fits well with regression tasks. Make sure that this model is not preferred when we have a way too large a dataset to deal with, as KNN is a distance-based algorithm which makes it high in cost when it comes to calculating the distance between two data points.
2. How does KNN approach the assigned task?
K-NN follows up a well-structured method to complete the assigned task, and I have tried to break it down into a few steps:
- Step-1: The first step is to choose the number of neighbors i.e., the K-variable, which changes based on the requirements and different tasks
- Step-2: So, we already have selected the number of neighbors. Now we need to find the Euclidean distance of those neighbors.
- Step 3: After calculating the Euclidean distance between those points, choose the nearest K neighbors based on the previous calculation
- Step-4: Now count the total number of data points in both categories from the selected K-Neighbors
- Step-5: The last step is to give the new data points to those categories where the number of K-Neighbors is maximum.
3. How to find the best value for K in the KNN algorithm?
First, we need to know what exactly is K value is; K is the numeric value that keeps the count of nearest neighbors, and we can’t have the hit, and trial method for multiple k values as the cost of calculation is pretty expensive hence we need to have certain guidelines to choose the optimal “K.”
- It is quite a domain-specific task that also requires an experience in a related field to choose the optimal K-value for the different problem statements, widely the most preferred value for K is supposed to be 5 (not a hard-coded number).
- If one is choosing a very small value for K (say k=1,2) for reducing the cost of computation, then, it will lead to a noisy model which will surely be prone to outliers in the model.
- Moderately large values for K are preferred, but when it is too large, then, it will lead to the underfitting condition.
4. How KNN is different from other classification algorithms in terms of its implementation?
Choosing KNN over another classification algorithm solely depends on our requirements. Suppose we are working on a task that requires flexibility in the model then we can go for KNN, whereas if efficiency is the priority then we can go for other algorithms like Gradient descent or Logistic Regression.
Note: Above answer can backfire on another question, so be prepared for it: How KNN is more flexible?
The main logic behind KNN flexibility is that it is a non-parametric algorithm so it won’t have to make any assumption on the underlying dataset. Still, at the same time, it is expensive in computation, unlike other classification algorithms.
5. How KNN and decision trees are different in terms of performance?
Both decision trees and KNN is non-parametric algorithms but are different in their way of delivering the results; some are as follows:
- When dealing with larger dataset, decision trees is faster than KNN because of its high computational drawback (when distance is being calculated)
- KNN is more accurate than decision trees as it scans the whole dataset closely.
- KNN is easy to implement comparatively with decision trees.
6. Why most of the time Euclidean distance is the preferred method for KNN?
For calculating distances in KNN, we have multiple options available like Chi-square, Minkowsky, cosine similarity measure, and so on. But Euclidean distance is a widely preferred method for calculating the distance as it returns us the shortest distance between two data points.
7. Why data normalization is an important step in KNN?
Before hitting the nail, let’s first understand which nail to hit i.e. what is normalization?
Normalization is the process where the whole dataset is scaled down within a specific range, mostly between 0 and 1. This turned out to be a necessary step while dealing with the KNN algorithm as it is a distance-based algorithm, so if the data points are not within a specific range, then different magnitudes can misclassify the data points in the testing phase.
8. What do you understand by the curse of dimensionality, and how KNN is affected?
The curse of dimensionality is considered to be checked frequently while working with the KNN model, whereas the dimension tends to increase, then the data turns to be more sparse, i.e., we often find tons of space in the datasets, which leads to the state of overfitting also which makes algorithm incapable to find the nearest neighbors. Ideally, as the number of dimensions increases, the space in the dataset should also increase exponentially (both should positively complement each other).
Linearization is one of the best techniques to break the curse of dimensionality – This is a bonus point I’ll suggest having more understanding of this term.
9. What would be the situations where KNN will perform poorly?
There are a few conditions where KNN will not perform based on our expectations, listing them below:
- When the data is very noisy or is not linearly separable.
- KNN, at times, can be costly in terms of computations for larger datasets.
- KNN is least preferred when datasets have multiple dimensions, as it leads to a curse of dimensionality.
Here we are in the last section of the article, by far we have discussed the Top 9 questions that are most frequently asked in an interview related to KNN, and this section will help you in revision and let you know what things to cover in brief regarding this hyper extensive algorithm.
- Firstly we saw where the KNN algorithm is most likely to be applicable then we found out the blueprint of how it works in the background after that, we discussed the most interesting part related to KNN, i.e., the K-value and how to find the optimal K value.
- In the next quarter of the article, we compared KNN with different algorithms, like how it differs from another classification algorithm in terms of implementation. Also, we saw how KNN and decision trees are different species in terms of performance later; we also noted why Euclidean distance is preferred for calculating distance.
- Last, we answered a few questions related to data normalization and discussed why it is important. Then, we look at the hottest topic, i.e., the curse of dimensionality and the method to deal with it. Lastly, listed down some points where KNN can underperform
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