knn in machine learning

Unraveling KNN in Machine Learning

one of the machine learning algorithms that has piqued my interest recently is K-Nearest Neighbors, or KNN for short.

Furthermore, in this article, I’ll share my insights into KNN, its applications, and how it works. So, let’s get started!

What is KNN in Machine Learning?

KNN is a simple yet powerful supervised learning algorithm we can use for classification and regression tasks.

Moreover, the main idea behind KNN is that similar data points tend to be closer together in feature space.

Why Do We Use KNN in Machine Learning?

It’s popular in machine learning because it:

  • Is easy to understand and implement
  • Adapts well to new data
  • Performs well with small datasets
  • Requires minimal training time
  • Is a non-parametric method, making no assumptions about the data distribution

KNN Algorithm: A Machine Learning Workhorse

KNN algorithm operates by finding the k nearest neighbors of a new data point and determining its class or value based on a majority vote or averaging.

It’s particularly useful in solving problems where:

  • The data is noisy
  • The decision boundary is irregular
  • The relationships between features are complex

KNN Algorithm Example: A Quick Illustration

Imagine we have a dataset containing information about fruits, with features like color, size, and texture.

In order to classify an unknown fruit, we can use KNN by finding its k nearest neighbors in the dataset and assigning the most common fruit label among them.

Example with code

Here’s an example of how to implement the KNN algorithm using Python and the sklearn library.

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# Load the Iris dataset
iris = load_iris()
X =
y =

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Normalize the data
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Create the KNN classifier
k = 3
knn = KNeighborsClassifier(n_neighbors=k)

# Train the classifier, y_train)

# Make predictions on the test set
y_pred = knn.predict(X_test)

# Calculate the accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"KNN accuracy: {accuracy:.2f}")

This code demonstrates how to use the KNN algorithm with sklearn library to classify the famous Iris dataset.

In order to train our KNN classifier model, we need to split the data into training and testing sets. After that we also need to normalize it.

Finally, we train the KNN classifier and evaluate it.

What is KNN Best Suited for?

KNN excels in scenarios where:

  • The dataset is small to moderately-sized
  • The decision boundaries are irregular
  • The problem involves multi-class classification or regression

However, it might struggle with large datasets and high-dimensional feature spaces.

KNN in Real Life: Practical Applications

KNN has a wide range of real-life applications, such as:

How Does KNN Work Step by Step?

To implement KNN, follow these steps:

  1. Choose the number of neighbors k and the distance metric.
  2. Calculate the distance between the new data point and all training data points.
  3. Select the k nearest neighbors.
  4. Determine the class or value of the new data point based on a majority vote or averaging.

What Distance is Used in KNN?

Various distance metrics can be used in KNN, including:

Moreover, the choice of distance metric depends on the problem and the nature of the data.


In conclusion, KNN is a versatile and intuitive algorithm that plays a significant role in machine learning.

Furthermore, its simplicity, adaptability, and effectiveness make it a popular choice for solving diverse problems in classification and regression.

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