Skip to main content

Understanding the K-Nearest Neighbors Algorithm (A Beginner's Guide part 7)

Understanding the K-Nearest Neighbors Algorithm (A Beginner's Guide)

Machine learning algorithms can seem complex, but breaking them down into simpler terms can make them more approachable. One such algorithm is the K-Nearest Neighbors (K-NN) algorithm, which is popular for its simplicity and effectiveness. In this blog, we'll explore what K-NN is, how it works, and some practical applications.

What is K-Nearest Neighbors?

K-Nearest Neighbors (K-NN) is a supervised learning algorithm used for classification and regression tasks. In simple terms, K-NN classifies data points based on the 'votes' of their nearest neighbors. It doesn't make any assumptions about the underlying data distribution, making it a non-parametric algorithm.

How Does K-NN Work?


The K-Nearest Neighbors algorithm operates based on the idea that data points that are close to each other tend to have similar properties or belong to the same class. Here’s a detailed step-by-step process of how K-NN works:

Step-by-Step Process

  1. Choose the Number of Neighbors (K)

    • The first step is to choose the number of neighbors, K. This is the number of closest data points the algorithm will consider when making a prediction. The choice of K can significantly impact the algorithm's performance.

  2. Calculate the Distance

    • For a given data point (test point), calculate the distance between this point and all other points in the training dataset. The most common distance metric used is the Euclidean distance, but other metrics like Manhattan distance or Minkowski distance can also be used.

    The Euclidean distance between two points (x1,y1)(x_1, y_1) and (x2,y2)(x_2, y_2) in a 2D space is calculated as:

    Euclidean Distance=(x2−x1)2+(y2−y1)2\text{Euclidean Distance} = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}

    For higher-dimensional spaces, the formula is:

    Euclidean Distance=∑i=1n(xi−yi)2\text{Euclidean Distance} = \sqrt{\sum_{i=1}^n (x_i - y_i)^2}

    where nn is the number of features.

  3. Identify the K-Nearest Neighbors

    • Once the distances are calculated, sort all the distances and identify the KK data points that are the closest to the test point. These are the K-nearest neighbors.

  4. Vote for the Class (for Classification)

    • For classification tasks, each of the K-nearest neighbors “votes” for their class, and the class with the most votes is assigned to the test point. This is known as majority voting.

    For example, if K=5 and 3 of the nearest neighbors belong to class A and 2 belong to class B, the test point is classified as class A.

  5. Calculate the Average (for Regression)

    • For regression tasks, the algorithm takes the average of the values of the K-nearest neighbors and assigns this average as the predicted value for the test point.

    For example, if K=5  and the values of the nearest neighbors are 10, 12, 15, 11, and 13, the predicted value for the test point would be:

    Predicted Value=(10+12+15+11+13)/5 ​=12.2

Detailed Example

Let's walk through a detailed example to illustrate these steps. Suppose we have a dataset of fruits with two features: weight and color (represented as a numerical value), and we want to classify a new fruit.

Training Data


Step-by-Step Process

  1. Choose k

    • Let's choose K=3

  2. Calculate the Distance


  • Identify the K-Nearest Neighbors

    • Sort the distances: 5 (Apple 1), 5.83 (Orange 3), 10 (Apple 3).
    • The 3 nearest neighbors are Apple 1, Orange 3, and Apple 3.

    • Vote for the Class

    • Among the 3 nearest neighbors, 2 are apples and 1 is an orange.
    • The new fruit is classified as an apple based on majority voting.

    By following these steps, K-NN provides a straightforward and intuitive way to classify new data points based on their similarity to existing data points.

    Example


    Let's say we want to classify whether a fruit is an apple or an orange based on its features like weight and color. We have a dataset with these features and their corresponding labels (apple or orange). To classify a new fruit, K-NN will:

    1. Calculate the distance between the new fruit and all other fruits in the dataset.
    2. Select the K-nearest fruits (e.g., K=3).
    3. Determine the majority class among these K-nearest fruits.
    4. Assign the new fruit to this majority class.


    Choosing the Right K

    Choosing the right value for K is crucial. A small K can be sensitive to noise in the data, while a large K can smooth out the predictions but may lose important details. A common approach is to use cross-validation to determine the optimal K value for your specific dataset.


    Advantages and Disadvantages

    Advantages

    • Simplicity: K-NN is easy to understand and implement.
    • No training phase: It doesn't require a training phase, making it fast for small datasets.
    • Versatility: Can be used for both classification and regression tasks.

    Disadvantages

    • Computationally expensive: For large datasets, the distance calculation can be time-consuming.
    • Storage requirements: Requires storing the entire dataset.
    • Sensitive to irrelevant features: Performance can degrade if irrelevant features are included.

    Practical Applications

    K-NN is used in various applications such as:

    • Recommendation systems: Suggesting products or content based on similar users' preferences.
    • Image recognition: Classifying images based on similarity to known images.
    • Medical diagnosis: Predicting diseases based on patient symptoms and historical data.

    Conclusion

    The K-Nearest Neighbors algorithm is a powerful yet simple tool in the machine learning toolbox. By understanding how it works and its applications, you can effectively use K-NN for various tasks. Remember to choose the right value of K and preprocess your data appropriately to achieve the best results.


    Sithija Theekshana 

    (bsc in Computer Science and Information Technology)

    (bsc in Applied Physics and Electronics)


    linkedin ;- www.linkedin.com/in/sithija-theekshana-008563229

    Comments

    Post a Comment

    Popular posts from this blog

    Understanding Machine Learning: A Beginner's Guide(part 1)

    Introduction Machine learning is a branch of artificial intelligence (AI) that is revolutionizing various industries, from healthcare to finance to technology. It enables computers to learn from data and make decisions or predictions without being explicitly programmed to perform specific tasks. In this blog post, we will delve into the basics of machine learning, exploring its significance, fundamental concepts, and how it works. The Significance of Machine Learning Machine learning has become a pivotal technology in the modern era due to its ability to process and analyze vast amounts of data more efficiently than traditional methods. Here’s why machine learning is so important: Automation of Tasks: Machine learning automates repetitive and mundane tasks, allowing humans to focus on more complex and creative endeavors. Data-Driven Decisions: By uncovering patterns and insights from data, machine learning helps businesses and organizations make informed decisions, leading to better ...
    Post a Comment
    Read more

    Supervised Learning and Unsupervised Learning in Machine Learning (A Beginner's Guide(part 2)

      Supervised Learning and Unsupervised Learning in Machine Learning Machine learning, a subset of artificial intelligence, involves training algorithms to learn from and make predictions or decisions based on data. Two fundamental types of machine learning are supervised learning and unsupervised learning. Understanding these concepts is crucial for anyone diving into the world of data science and machine learning. Supervised Learning Supervised learning is a type of machine learning where the model is trained on a labeled dataset. This means that each training example is paired with an output label. The goal is for the algorithm to learn a mapping from inputs to outputs so it can make accurate predictions on new, unseen data. Key Concepts Labeled Data : In supervised learning, the dataset consists of input-output pairs. For example, a dataset for a spam detection algorithm might include emails (inputs) and labels indicating whether each email is spam or not (outputs). Training Pro...
    Post a Comment
    Read more

    Spam Mail Prediction using Machine Learning

     Spam Mail Prediction using Machine Learning This project involves building a spam mail detector using Python within the Google Colab environment. By leveraging machine learning techniques, we aim to automatically classify emails as either spam or legitimate. The detector will enhance user security by filtering out potentially harmful emails. Source code(with describtion) Importing the Dependencies import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score Importing Libraries: The code begins by importing necessary libraries such as NumPy, Pandas, scikit-learn's train_test_split , TfidfVectorizer , LogisticRegression , and accuracy_score from sklearn.metrics . Data Preparation: It implies that you have a dataset containing email content along with labels indicating whether each emai...
    Post a Comment
    Read more