Services
Technology Web App Development Mobile App Development Windows App Development Database Development UI/UX Development Cloud Development SEO Digital Marketing Guest Posting Content Writing Artificial Intelligence Data Science ERP Development CRM Development CMS Development LMS Development Business Listing Blockchain Technology Startup Promotion
Products
Data Converter Cleaner TUC - Unit Converter Import Export Survey Manager Tweet Controller
Developer
Article Blog Forum Interview Beginner News Startup Quiz
Users
Career
Apply Job Internship
Pricing
Login Sign Up Get a Quote

forum

home / developersection / forums / what is overfitting?
Ask Question
Ravi Vishwakarma
Ravi Vishwakarma

IT-Hardware & Networking

Ravi Vishwakarma is a dedicated Software Developer with a passion for crafting efficient and innovative solutions. With a keen eye for detail and years of experience, he excels in developing robust software systems that meet client needs. His expertise spans across multiple programming languages and technologies, making him a valuable asset in any software development project.

Message

2 Answers

Anubhav Sharma
Anubhav Sharma 27 Apr 2026
Report Abuse

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

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.”

Amrith Chandran
Amrith Chandran 02 Mar 2026
Report Abuse

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.

MindStick Training