KNN stands for K-Nearest Neighbors, a 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 datasets or even the curse of dimensionality. While working with the KNN model as creating a model is not a tedious task, but dealing with its drawbacks and coming back with a solution is! In this article, we’ll discuss some of the most asked and tricky questions related to the KNN algorithm. So let’s get started!
The K-nearest neighbors (KNN) algorithm is a supervised machine learning method that makes predictions based on how close a data point is to others. It’s widely used for both classification and regression tasks because of its simplicity and popularity.
If KNN is used as the primary model, one needs to have sufficient domain knowledge of the problem statement they’re 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.
Feature | KNN (K-Nearest Neighbors) | K-Means Clustering |
---|---|---|
Type | Supervised Learning | Unsupervised Learning |
Purpose | Classification or Regression | Clustering (grouping similar data) |
How it works | Predicts label based on nearby neighbors | Finds clusters by minimizing distances to centroids |
KNN follows a well-structured method to complete the assigned task. Here is the general workflow:
First, we need to know what exactly is the K value. 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.”
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.
Both decision trees and KNN are non-parametric algorithms, but are different in their way of delivering the results. Here are the primary differences:
For calculating distances in KNN, we have multiple options available, like Minkowski, 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.
Data 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.
The curse of dimensionality is when the dimensions tend to increase, then the data becomes more sparse, i.e., we often find tons of space in the datasets, which leads to the state of overfitting, also making the algorithm incapable of finding 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.
There are a few conditions where KNN will not perform based on our expectations, listed below:
By far we have discussed the common questions that are frequently asked in an interview related to KNN. This will help you in revision and let you know what things to cover briefly regarding this algorithm. If you’re interested in more questions, you can read through the list of 30 interview questions on KNN.
This article was published as part of the Data Science Blogathon.