---
title: "What is overfitting?"  
description: "What is overfitting?"  
author: "ICSM Computer"  
published: 2026-02-27  
updated: 2026-04-27  
canonical: https://www.mindstick.com/forum/162038/what-is-overfitting  
category: "artificial intelligence"  
tags: ["artificial intelligence"]  
reading_time: 3 minutes  

---

# What is overfitting?

**What is [overfitting](https://www.mindstick.com/articles/336961/explain-the-concept-of-overfitting-and-underfitting-in-machine-learning)?**

## Replies

### Reply by Anubhav Sharma

**Overfitting** is when a machine learning model learns the **training data too well**, including noise and random patterns, so it performs **great on training data but poorly on new (unseen) data**.

## Simple Example

Imagine you memorize answers to past exam questions instead of understanding the concepts.\
You score high on the same questions—but fail when questions change.

That’s overfitting.

## How It Happens

In Machine Learning, overfitting occurs when:

- Model is **too complex** (too many parameters)
- Dataset is **too small**
- Model trains **too long**
- Noise in data is treated as real pattern

## Visual Idea

- **Good fit:** captures general trend
- **Overfit model:** fits every point exactly (zig-zag pattern)

## Key Symptoms

- Very **high training accuracy**
- Low **validation/test accuracy**
- Model fails in real-world scenarios

## Example

```plaintext
Training Accuracy = 99%
Test Accuracy     = 65%
→ Overfitting
```

## How to Prevent Overfitting

### 1. Use More Data

More data helps the model generalize better.

### 2. Simplify the Model

Reduce layers (in deep learning)

Use fewer features

### 3. Regularization

Techniques like:

- L1 / L2 regularization
- Dropout (in neural networks)

### 4. Cross-Validation

Split data into multiple parts to validate performance.

### 5. Early Stopping

Stop training when validation error starts increasing.

### 6. Data Augmentation

Create variations of data (common in image ML).

## Related Concept

- **Underfitting** → Model too simple, performs poorly everywhere
- **Overfitting** → Model too complex, memorizes data

## In One Line

## Overfitting = “Memorizing” instead of “Learning.”

### Reply by Amrith Chandran

**Overfitting** is a phenomenon in machine learning where a model learns the training data **too well**, including its noise or random fluctuations, instead of capturing the underlying patterns that generalize to new, unseen data.

In simpler terms, the model performs **very well on training data** but **poorly on test or real-world data** because it has memorized specifics rather than learned general rules.

#### Key Characteristics of Overfitting

**High training accuracy, low test accuracy:**\
The model predicts the training examples almost perfectly, but fails on new examples.

**Excessive model complexity:**\
Very deep neural networks or high-degree polynomial models can fit every tiny variation in the training data.

**Sensitivity to noise:**\
The model treats random fluctuations in the training data as meaningful patterns.

#### Example

Imagine trying to fit a curve through a set of points:

A **simple model** (like a straight line) might not pass through all points but captures the overall trend.

A **complex model** (like a high-degree polynomial) passes through every point exactly, including outliers.

The complex model **overfits** because it captures noise instead of the general trend.

#### How to Prevent Overfitting

**Use more data:** More training examples help the model learn general patterns.

**Regularisation:** Techniques like L1/L2 penalties or dropout prevent overly complex models.

**Simpler models:** Reduce the number of parameters or use less complex algorithms.

**Cross-validation:** Helps detect overfitting by evaluating the model on unseen subsets.

**Early stopping:** Stop training when test performance stops improving.

**In short,** Overfitting is when a model **memorizes** instead of **learning**, sacrificing its ability to generalize to new data.


---

Original Source: https://www.mindstick.com/forum/162038/what-is-overfitting

Copyright © MindStick Software Pvt. Ltd. This Markdown version is provided for developers, AI systems, and offline reading.