KNN Algorithm

-

KNN stands for K-Nearest Neighbour

It is used to solve both Classification & Regression problems.

1. KNN FOR CLASSIFICATION



Here, we have one group of Green points and one group of Red points.

Now, for yellow data point, we want to know whether it will belong to Red or Green group.

Let's say, k=3 

We will measure the distance and get 3 nearest data points from yellow.

As you can see in the figure, 3 nearest data points will include 2 Green and 1 Red. 

Hence, we will put it in Green category.

We measure the distance by 2 methods :

1) Eucledian Distance :

2) Manhanttan Distance :


Manhattan Distance = | x 1 − x 2 | + | y 1 − y 2 |




2. KNN FOR REGRESSION




If k=4, it will be average of 4 nearest data points.

How to Choose the K-factor?

Have a look at the below graphs for training error and validation error for different values of k.




First Fig shows error in training set and Second Fig shows the error in validation set.

For smaller values of k, training error is near to 0 and validation error is too high, it means model is overfitting. 

For higher values of k, both training and validation error is high. 
If you observe closely, the validation error curve reaches a minimum at a value of k = 9. This value of k is the optimum value of the model
Researchers typically use the elbow curve, named for its resemblance to an elbow, to determine the k value.

KNN works very bad for 2 situations :

1) Outliers

Consider black data points are working as an outliers. It will choose nearest points from outliers as well.


2) Imbalanced Data

K-Nearest Neighbors (KNN) algorithm can struggle with imbalanced datasets because it relies on the majority class to determine the class of a new data point.
In imbalanced datasets, where the minority class has fewer instances, their representation in the feature space might not be accurately reflected in the distance calculations, leading to misclassification.


Comments

Post a Comment

Popular posts from this blog

Extracting Tables and Text from Images Using Python

-
Image
In this blog, we'll explore a complete Python solution that detects and extracts tables and text from images using libraries like Transformers, OpenCV, PaddleOCR, and easyOCR. This step-by-step breakdown includes code to detect tables, extract content from individual table cells, and retrieve any remaining text in the image. Overview When working with scanned documents, such as invoices or forms, it is essential to accurately extract both structured information (like tables) and unstructured text. The approach we’ll explore uses Microsoft's pretrained object detection model to locate tables and OCR techniques to extract the text from both table cells and the rest of the image. Steps: 1. This code first detects table using microsoft's model. and save that image which contains detected table only  2. After that, from the detected table , we make a seperate image for each cell.  3. Then we read text from the image of each cell  4. Now, to read the extra texts except for the ...
Read more

Positional Encoding in Transformer

-
Image
1. Why Position Matters in Transformers? Transformers rely on self‑attention, which processes tokens in parallel. This means, unlike RNNs, they don’t inherently know the order of words. So, sentences like “Ravi killed the lion” vs. “The lion killed Ravi” would look identical to a vanilla Transformer—clearly problematic! 🧪 Idea #1: The Naïve Approach A simple fix would be to add index/position of the token in an embedding vector. Issues: Unbounded values: Position IDs can become huge (e.g. 100,000+ in long texts), destabilizing training. Discrete steps: Sharp jumps between integers disrupt gradient flow. 🧪 Idea #2: Normalize the Position Numbers What if we divide the position numbers by a constant to make them small and smooth? That helps a bit—values don’t explode anymore. Issues: Now, if you observe, in both the sentences, the word at second position has got the different values. 1 for sentence1, and 0.5 for sentence2. so, the Neural network will get confused while training, what a...
Read more

Chain Component in LangChain

-
Image
LangChain is a powerful framework for building applications with Large Language Models (LLMs). One of its core features is the Chain Component , which allows developers to link multiple operations together to create complex workflows. In this blog, we'll explore different types of chains in LangChain and demonstrate their usage with code examples. 1️⃣ LLMChain (Basic Single-Step Chain) 🔹 Executes a single LLM call based on a prompt. 🔹 The simplest chain type. ✅ Use Case: When you need a single prompt-response from an LLM. Example: 2️⃣ SequentialChain (Step-by-Step Execution) 🔹 Executes multiple chains in sequence where each step depends on the output of the previous step. ✅ Use Case: Multi-step processes where each step depends on the previous output . 📌  Example: Blog Outline → Content Expansion → Summary Example: 3️⃣ SimpleSequentialChain (Basic Sequential Execution) 🔹 Similar to SequentialChain, but only passes the last step’s output forward (less flexible). 🔹 ...
Read more