Definition
In machine learning, hyperparameters are settings or configurations in a model that are not learned during the training process but are rather set before the start of the training process. They influence how a model is trained and can significantly affect its performance and accuracy [1].
In other words,
anything in machine learning and deep learning that you set their values or
choose their configuration before training begins and whose values or
configuration will remain the same when training ends is a hyperparameter. Examples
of hyperparameters include the learning rate, batch size, number of hidden
layers, and regularization strength (e.g., dropout rate).
For example, imagine
that you are establishing a tailoring shop in Aba's Ariaria Market. Before you
start sewing for customers, you have to decide on certain predefined settings
that affect how your fashion business will function and attract clients. This may
include your specialization strategy such as focus on men's native wear vs
women's ankara dresses, which equipment you'll invest in (basic sewing machine
vs industrial embroidery equipment), how many tailors you'll employ (work solo
vs hire 3 assistants), and your customer target (mass market affordable pricing
vs high-end custom designs). These are your hyperparameters, and they affect
the customer base and profit margins of your tailoring business throughout the
year while remaining relatively fixed once your brand positioning is set.
Examples of
hyperparameters [2].
Origin
The origin of
hyperparameters can be traced to 1986 when three researchers (David Rumelhart,
Geoffrey Hinton, who would eventually be known as the Godfather of AI, and
Ronald Williams) published a paper on what they call a “new learning
procedure.” The idea was to create a procedure that would adjust the variables
of a mathematical equation to minimize the error between the output it gives
and the actual output. Formerly known as learning rate, it was later changed to
hyperparameter.
The optimal
values for hyperparameters are usually found through experimentation or
techniques like grid or random search. Many years later, the concepts of hyperparameters
continue to be integral to training neural networks.
In 2012, multi-decade
research on training neural networks culminated into a paper titled Practical
Recommendations for Gradient-Based Training of Deep Architectures by Yoshua
Bengio. This paper mainly focused on the training/learning process itself,
where hyperparameters play a key role [3].
Context and
Usage
Hyperparameters
play a key role numerous industries like tech, finance, healthcare, and
manufacturing. Properly adjusting these parameters help AI systems to achieve
higher accuracy, faster training times, and more robust generalization
capabilities.
For example, hyperparameters
are vital for tasks such as image recognition, natural language processing,
fraud detection, and predictive maintenance, ensuring that AI algorithms can
effectively address complex problems and deliver valuable insights.
In natural
language processing, hyperparameter tuning can be utilized to improve the
performance of language models for tasks like sentiment analysis or language
translation.
In predictive
maintenance, hyperparameters can be adjusted to optimize the detection of
potential equipment failures and reduce downtime in manufacturing plants. Properly
tuning these parameters enables AI systems to be trained more efficiently and
effectively to provide accurate predictions and actionable recommendations [4].
Why it Matters
Hyperparameters choices
has a drastic effect on the performance of the AI model just as how driving at
a certain speed and in a certain direction can impact how smoothly a car moves.
Carefully selection and tuning of these hyperparameters is essential for enabling
AI systems learn effectively and make accurate predictions.
Related Model
Training and Evaluation Concepts
- Hyperparameter Tuning: Process of finding optimal hyperparameter values to improve model performance
- Inference: Process of using a trained model to make predictions or generate outputs on new data
- Instruction Tuning: Training method that teaches models to follow specific instructions and commands
- Loss Function: Mathematical measure of how far a model's predictions are from actual values
- Model Compression: Techniques for reducing model size and computational requirements while maintaining performance
In Practice
A real-life case study of hyperparameters can be seen in the case of C3 AI platform. C3 AI executes of hyperparameter optimization with substantial computing resources through worker nodes in its environment. The platform supports both manual early stopping—allowing users to halt unpromising iterations—and automated early stopping that terminates the search once user-defined performance thresholds are met. C3 AI Platform Hyperparameter Optimization also offers model persistence options such as “keep all trained” or “keep best trained,” with custom validation options for hold outs and non-time-series k-folds. Finally, users can view results during and after a search that are organized by hyperparameter combinations [5].
- Infomaticae. (2025). Hyperparameters in Machine Learning: A Comprehensive Guide.
- Analytixlabs. (2026). Comprehensive Guide on Hyperparameters: Optimization, Examples, and more.
- XQ. (2024). Explained: Hyperparameters in Deep Learning
- Iterate AI. (2025). Hyperparameter: The Definition, Use Case, and Relevance for Enterprises.
- C3 AI. (2026). Hyperparameters.