Hyperparameter Tuning in Machine Learning: A Complete Guide

Machine learning is one of the most revolutionary technologies of our time. These adaptable, continuously improving AI models can automate and streamline many tasks, but ensuring they can do this effectively can be challenging. Hyperparameter tuning in machine learning helps ML developers make the best model possible.

Many professionals today recognize the importance of training a machine learning model. However, it’s also crucial to tweak the training process itself to make ML development as smooth and effective as possible. That’s where hyperparameters and hyperparameter tuning come in.

Basics of Hyperparameters in Machine Learning

Before getting into how to approach machine learning hyperparameter tuning, it’s important to understand some basics. Here’s what you need to know about hyperparameters before getting started.

What Are Hyperparameters?

A hyperparameter is a value that defines a machine learning model’s architecture. Think of them as settings you adjust before training a model that control how the model learns from its data. Examples of hyperparameters include:

• The model’s learning rate
• The number of branches in a decision tree
• The number of trees in a random forest model
• The overall model architecture
• The number of layers in a neural network

It’s important to distinguish between hyperparameters and parameters. While these terms sound remarkably similar, especially when many people call hyperparameters model parameters, they’re separate things.

Machine learning parameters are the values your model pulls from training data. They change from application to application, but your hyperparameters remain the same. Think of parameters as what the model learns and hyperparameters as how it learns them.

How Do Hyperparameters Impact Model Performance?

Hyperparameters are an important part of machine learning development because they influence how a model arrives at conclusions. If the hyperparameters are too specific — a problem known as overfitting — the model won’t be versatile. If they’re too vague — something called underfitting — the model won’t be accurate.

Getting these settings right also impacts the training process. Long training timelines are already one of AI’s biggest limitations, and unoptimized hyperparameters will make it take even longer because the model won’t learn as efficiently. Consequently, developers must approach these settings carefully to make the most of training time and costs.

What Is Hyperparameter Tuning in Machine Learning?

Because machine learning is highly complex, data scientists almost never set the perfect hyperparameter values on the first try. Rather, optimizing these factors requires several small adjustments over multiple trials to get them just right. This process is known as hyperparameter tuning in machine learning.

Just as the ML training process tunes parameters, hyperparameter tuning optimizes the ML architecture itself. Through this process, ML developers find what values produce the ideal training environment for their model to reach their goals. Once they’re sure the model learns the way it should, they can refine it further through training to apply it to real-world scenarios.

Hyperparameter Tuning Techniques

There are many ways to find the ideal hyperparameters, each with unique advantages and disadvantages. Here are a few of the most popular hyperparameter tuning techniques.

Grid Search

A grid search is the most straightforward hyperparameter tuning method, making it one of the most common. In this technique, developers build an ML model for each possible combination of hyperparameter values. Then, they test each one and compare the results to identify the best.

The biggest advantage of a grid search is that it provides an exhaustive list of possibilities, making it easy to identify the best path forward. However, because it’s so comprehensive, it can be inefficient and expensive. Considering more than half of AI adopters already say they’ve spent more than expected on model production, that inefficiency is a significant obstacle.

Random Search

The random search approach to hyperparameter tuning is more efficient. As the name suggests, this technique uses a randomly selected set of values instead of testing every possible iteration. How open that random selection is can vary between projects. Developers can define the sampling distribution for each hyperparameter and how many combinations to build as specifically as they want.

The most obvious benefit of a random search is that teams can complete it in far less time than a grid search. That efficiency does come at the cost of less confidence that you’re getting the best results possible, though.

Bayesian Optimization

For more complex hyperparameter tuning, many developers turn to Bayesian optimization. This strategy applies Bayes’ Theorem, a mathematical concept that updates probabilities given new evidence, to refine the optimization process. Instead of running tests in isolation like random and grid searches do, Bayesian optimization uses the results of one experiment to improve the next.

Because this approach fine-tunes hyperparameters based on the results of a previous model, it’s not entirely random. This step-by-step method also helps find the best option without testing every probability as a grid search does. However, if there are a lot of hyperparameters to consider or balancing them is more challenging, Bayesian optimization can take a long time.

Which Is Best?

Hyperparameter tuning in machine learning covers a lot of variability, so there’s no one answer to which approach is best. Rather, it depends on the specific needs of the ML project at hand.

Grid searches provide the most conclusive answers and are relatively simple to perform but require a lot of time. Random searches may not produce the best possible tuning, but they’re faster. Bayesian optimization lands in the middle in terms of time and accuracy but can be computationally complex and is best suited for simpler ML models.

Automated Hyperparameter Tuning

Regardless of the specific approach a project takes, developers should consider automated hyperparameter tuning. This method applies automated machine learning (AutoML) to streamline the process.

Automated tuning can create and test models in a grid search, random search or Bayesian approach autonomously. Human devs only have to specify their goals, which hyperparameters to adjust and what method to use. Because AutoML can develop models much faster than humans, it makes each of these approaches more computationally, financially and time efficient.

Of course, that speed does come at a cost. If the AutoML algorithm itself isn’t ideal, it may incorrectly identify the best hyperparameter values. It may also be difficult to tell how the model arrived at its conclusions, limiting traceability in the project.

Hyperparameter Tuning Is a Crucial Part of Machine Learning

Hyperparameter tuning in machine learning is an essential part of making reliable ML models. Once teams understand this process and its importance, they can make the most of this technology.

Fine-tuning hyperparameters, like machine learning as a whole, can be challenging, but the results of an effective job are hard to argue with. Learning more about this process is the first step to fully capitalizing on its potential.