---
title: "SVM (Support Vector Machine) kya hai?"  
description: "SVM (Support Vector Machine) kya hai?"  
author: "Ravi Vishwakarma"  
published: 2026-03-06  
updated: 2026-04-12  
canonical: https://answers.mindstick.com/qa/116384/svm-support-vector-machine-kya-hai  
category: "artificial-intelligence"  
tags: ["artificial intelligence"]  
reading_time: 2 minutes  

---

# SVM (Support Vector Machine) kya hai?

## Answers

### Answer by Anubhav Sharma

**SVM ([Support Vector](https://www.mindstick.com/forum/162045/what-is-support-vector-machine-svm) Machine)** ek powerful **[machine learning](https://www.mindstick.com/articles/44690/how-shopping-is-evolving-with-machine-learning) algorithm** hai jo mainly **[classification](https://www.mindstick.com/blog/304034/regression-and-classification-in-machine-learning-difference)** aur **regression** problems solve karne ke liye use hota hai.

## Simple Language mein samjho:

SVM ka goal hota hai data ko alag-alag categories (classes) mein divide karna — ek **best boundary (line ya plane)** ke through.

- Is boundary ko **Hyperplane** kaha jata hai.

## Kaise kaam karta hai?

Maan lo tumhare paas 2 types ke data hain:

- Red points
- Blue points

SVM ek aisi line draw karta hai jo:

- Dono classes ko alag kare
- Dono classes se **maximum distance** banaye

Is distance ko **Margin** kehte hain\
Aur jo points boundary ke sabse paas hote hain unhe **Support Vectors** kehte hain

## Key Concepts:

### 1. Hyperplane

- Ek decision boundary (2D mein line, 3D mein plane)

### 2. Margin

- Hyperplane aur nearest data points ke beech ka distance
- SVM maximum margin choose karta hai (better accuracy ke liye)

### 3. Support Vectors

- Wo data points jo boundary ke sabse paas hote hain
- Ye model ko define karte hain

## Types of SVM:

### Linear SVM

- Jab data easily ek [straight line](https://answers.mindstick.com/qa/35148/which-is-the-only-us-state-not-to-have-a-straight-line-in-its-border) se separate ho jaye

### Non-Linear SVM

- Jab data complex ho
- Is case mein **Kernel Trick** use hota hai

## Kernel Trick kya hota hai?

Agar data linearly separable nahi hai, to SVM usse higher dimension mein convert karta hai.

Popular Kernels:

- Linear
- Polynomial
- RBF (Radial Basis Function)

## Real-life Example:

- Email [spam detection](https://www.mindstick.com/news/4033/airtel-launches-ai-powered-spam-detection-solution-processing-1-trillion-records-in-real-time)
- [Face recognition](https://www.mindstick.com/forum/157846/what-is-face-recognition-how-is-it-used-in-computer-vision)
- Text classification

## Advantages:

- High accuracy
- Small dataset pe bhi achha kaam karta hai
- [Overfitting](https://www.mindstick.com/forum/34564/underfitting-and-overfitting) ka chance kam

## Disadvantages:

- Large dataset pe slow ho sakta hai
- Kernel selection tricky hota hai

## Short Definition:

**SVM ek supervised machine learning algorithm hai jo data ko best possible boundary (hyperplane) ke through separate karta hai, maximum margin maintain karte hue.**


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Original Source: https://answers.mindstick.com/qa/116384/svm-support-vector-machine-kya-hai

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