Overfitting and Underfitting in Machine Learning

Just imagine a machine learning model either failing to learn anything useful at all or memorizing every detail. This challenge forms the crux of underfitting and overfitting — the two most common obstacles in machine learning.

This is where professionals like machine learning engineers can be so valuable, especially in tech-oriented countries like Singapore. This is also why they earn between SGD 54,150 and SGD 168,179 per year, with an annual average base salary of SGD 73,513.

In this blog, we will discuss overfitting and underfitting in machine learning in great detail, explore their common causes and signs, and learn how to prevent and mitigate these issues in such work.

Source: Indeed, as of June 9, 2026

Understanding Overfitting and Underfitting in Machine Learning Models

The following table attempts to explain the difference between overfitting and underfitting in machine learning models in various aspects:

Aspect	Overfitting	Underfitting
Model behavior	Learns too much from training data	Learns too little from training data
Training accuracy	Very high	Low
Testing accuracy	Poor	Poor
Model complexity	Too complex	Too simple
Main cause	Noise memorization	Insufficient learning
Example	Memorizing answers	Not studying enough
Solution	Regularization, more data	Better features, complex models

1 What Is Overfitting in Machine Learning?

So, what is overfitting in machine learning? Overfitting happens when a machine learning model learns the training data excessively well, including its random fluctuations and noise, instead of the underlying pattern.

Also Read: Future Scope of AI: What to Expect in 2026 and Beyond

2 What Is Underfitting in Machine Learning?

Underfitting occurs when a machine learning model is too simple to learn the underlying structure of the training data.

3. Why Do These Problems Matter in Real-World Artificial Intelligence (AI) Applications?

In real-world AI applications, underfitting and overfitting matter because they directly lead to financial loss, damaged user trust, and safety risks.

4 Bias vs. Variance: The Core Concept Behind Both Problems

Bias and variance are the two components of a model’s prediction error that dictate if it will overfit or underfit.

Bias is the main reason behind underfitting, while variance is the prominent cause of overfitting.

Common Causes and Signs of Overfitting and Underfitting

We have already learned the differences between overfitting and underfitting in machine learning. We will now look at their common causes and signs.

Causes of Overfitting

The most prominent causes of overfitting in machine learning models are:

Excessive model complexity
Insufficient training data
Noisy and uncleaned data
Overtraining
Lack of regularization

Causes of Underfitting

The main causes of underfitting in machine learning models are:

Insufficient model complexity
Over-regularization
Inadequate feature engineering
Premature stopping
High data noise and bad clean-up

Signs Your Machine Learning Model Is Overfitting or Underfitting

The most prominent signs of underfitting and overfitting in machine learning are:

Overfitting	Underfitting
Diverging loss curves Large performance gapExtreme weight coefficients Deep and unstable treesFlawless training predictions	High training error Flat validation curvesNo performance gapOverly simplistic predictions Sub-baseline performance

Real-Life Example of Overfitting vs. Underfitting

A real-life example of overfitting is a student who relies on rote memorization, while underfitting can be compared to a student who does not study enough.

Also Read: How to Become a Platform Engineer in Singapore: Complete Roadmap to Salary, Skills & Career Guide (2026)

How to Prevent Overfitting and Underfitting in Machine Learning?

We will now look at how we can prevent overfitting and underfitting in machine learning.

1 Techniques to Reduce Overfitting

The various techniques you can use to reduce overfitting include data-centric techniques, model and structural constraints, algorithm and optimization tweaks, and validation strategies.

In terms of data-centric techniques, you can train on more data, augment it, and clean it as well.

2 Techniques to Reduce Underfitting

The different techniques that can be used to reduce underfitting include model and structural adjustments, feature engineering, and optimization and training tweaks.

Also Read: How to Build a Generative AI Portfolio That Solves Real Business Problems

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FAQs On Beginner’s Guide to Overfitting and Underfitting in Machine Learning

What are overfitting and underfitting in machine learning?

Overfitting and underfitting in machine learning occur when a model either fails to learn anything useful at all or memorizes every detail. These are the most common obstacles in modern machine learning.

What causes overfitting in machine learning models?

The most prominent causes of overfitting in machine learning models may be enumerated as follows:
Excessive model complexity
Insufficient training data
Noisy and uncleaned data
Overtraining
Lack of regularization

How can beginners identify underfitting in a model?

The following are the most prominent signs that beginners can use to detect underfitting in a machine learning model:
High training error
Flat validation curves
No performance gap
Overly simplistic predictions
Sub-baseline performance

What is the difference between bias and variance?

Bias is the error introduced when a complex, real-world problem is approximated by a model that is too simple. However, variance is the error introduced by a model’s extreme sensitivity to small fluctuations in the training dataset.

Why does overfitting reduce model accuracy on new data?

Overfitting reduces model accuracy on new data because the model mistakes random noise, coincidental patterns, and outliers in the training set for universal truths.