Unlocking the Power of KNN: A Comprehensive Guide to Classification and Regression

5 min readJul 13, 2024

K-Nearest Neighbors (KNN) is a versatile and straightforward machine learning algorithm used in supervised learning in both regression and classification tasks. To understand KNN, it’s essential to have a basic grasp of supervised learning, and the distinctions between regression and classification.

Let’s take an overview of Supervised learning and regression and classification, what it is:

Supervised Learning: An Overview

Supervised Learning is a type of machine learning where the algorithm learns from labeled training data. The goal is to predict the output (target variable) for new, unseen data based on the input-output pairs from the training set.

like data set you have X -> Y ( for X inputs you have Y label which is output )

Regression: Predicts continuous outcomes. For example, predicting house prices based on features like area, number of rooms, and location.
Classification: Predicts discrete outcomes. For example, classifying emails as spam or not spam based on their content.

K-Nearest Neighbors (KNN): A Detailed Exploration:

KNN is a non-parametric algorithm ( It doesn’t make any assumptions about the underlying distribution of the data. It doesn’t require a specific functional form or parameters to model the data. It’s flexible and can handle different types of data distributions ) and instance-based learning algorithm ( KNN is a type of lazy learning algorithm, which means it doesn’t learn a model from the training data beforehand. Instead, it waits until a new instance (or example) is presented, and then it learns from the training data to make a prediction for that specific instance) that can be used for both regression and classification tasks.

Here’s is the step by step explanation about how to apply KNN on the dataset:

Data Collection: Gather the dataset that contains labeled instances (features and target variables).

Choosing the Number of Neighbors (K): Select the number of nearest neighbors to consider (K). This is a crucial hyperparameter in the KNN algorithm. Choosing the right K is often done through cross-validation.

Distance Metric: Choose a distance metric to measure the similarity between instances. Commonly used distance metrics include Euclidean distance, Manhattan distance, and Minkowski distance.

Finding Nearest Neighbors: For a new data point, calculate the distance to all points in the training set and identify the K-nearest neighbors.

Making Predictions:

Classification: The class label of the new data point is determined by the majority class among the K-nearest neighbors means taking mode.
Regression: The predicted value is the average (or weighted average) of the target values of the K-nearest neighbors.

Let’s understand more better by a simple example:

Consider the following dataset with five data points:

We want to predict the target value for a new data point F with the features (X1 = 4, X2 = 4) using KNN.

Choosing K: Let’s choose K=3.
Calculating Distances: Compute the Euclidean distance from point F to all other points.

Finding Nearest Neighbors: Identify the three nearest neighbors based on the calculated distances. The nearest neighbors are C (1.41), B (2.24), and D (2.24).
Making the Prediction: For classification, we take a majority vote of the neighbors’ class labels, as label for C is 1, D is 1, and B is 0.

The majority class is 1, so the predicted class for F is 1.

so, it’s for classification, and in the case of regression we do the same, just make predictions by taking the mean of all neighbor’s class.

Scenarios Where KNN is a Good Choice

KNN is well-suited for the following scenarios:

Low-Dimensional Data: KNN works best with low-dimensional data. High-dimensional data can dilute the distance metrics, making it harder to identify nearest neighbors accurately.
Small to Medium-Sized Datasets: KNN is computationally intensive, so it’s more effective with smaller datasets. Large datasets can significantly slow down the prediction process.
Non-Parametric Nature: KNN doesn’t assume any specific distribution for the data, making it flexible for a variety of problems where the data does not follow a known distribution.

Advantages of KNN

Simplicity: KNN is easy to understand and implement, making it an excellent choice for beginners.
No Training Phase: KNN is a lazy learner, meaning it does not require a training phase, which can save time.
Versatility: KNN can be used for both classification and regression tasks.
Flexibility: No assumption about the underlying data distribution is needed.

Disadvantages of KNN

Computationally Expensive: KNN can be slow with large datasets since it calculates the distance of the new instance to all instances in the training set.
Memory Intensive: KNN requires storing the entire dataset, which can be a drawback for large datasets.
Sensitive to Irrelevant Features and Noise: KNN can be affected by irrelevant features and noisy data, impacting the algorithm’s performance.
Curse of Dimensionality: In high-dimensional spaces, the concept of distance becomes less meaningful, and KNN may perform poorly.

Now I will show the implementation of KNN using scikit-learn.