---
title: "What is overfitting in machine learning and how can it be prevented?"  
description: "What is overfitting in machine learning and how can it be prevented?"  
author: "Harry"  
published: 2024-04-24  
updated: 2024-05-01  
canonical: https://www.mindstick.com/forum/160639/what-is-overfitting-in-machine-learning-and-how-can-it-be-prevented  
category: "artificial intelligence"  
tags: ["python", "artificial intelligence", "ai", "machine learning"]  
reading_time: 1 minute  

---

# What is overfitting in machine learning and how can it be prevented?

What is the [difference between supervised](https://www.mindstick.com/forum/158510/what-is-the-difference-between-supervised-learning-and-unsupervised-learning) and unsupervised [learning](https://www.mindstick.com/articles/126221/instructional-design-for-elearning-why-it-is-so-important)?

## Replies

### Reply by Bhavesh Badani

- **Overfitting**: Generally Occurs when a model learns the training data too well or too deep in a menner such that it also capture noise and specific details that don’t generalize to new data. Signs of overfitting include high training accuracy but poor performance on unseen data.
- **Ways to prevent overfitting**:

   - **Regularization**: Directly Introduce penalties for complex models (e.g., L1 or L2 regularization), which may regulate overfitting.
   - **Early Stopping**: Monitor validation performance and stop training as soon as it starts to degrade.
   - **Feature Selection**: Choose relevant features and avoid unnecessary complexity. This is very helpful step when it comes to prevent overfitting.


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Original Source: https://www.mindstick.com/forum/160639/what-is-overfitting-in-machine-learning-and-how-can-it-be-prevented

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