A Beginner’s Guide to Hyperparameter Optimization in Machine Learning

Key takeaways

Hyperparameter optimization is the process of adjusting hyperparameters during machine learning training to find those that produce the best output. Here are some important things to know:

• The global machine learning market is projected to grow from $55.80 billion in 2024 to $282.13 billion by 2030 [1].

• Hyperparameter optimization in machine learning improves model performance by increasing accuracy and training efficiency.

• You can enter the machine learning field in careers such as a data scientist, machine learning engineer, or NLP engineer.

Discover how hyperparameter optimization can help you develop optimal results from your machine learning models. If you’re ready to start earning credentials toward a career in machine learning, enroll in the IBM Machine Learning with Python and Scikit-learn Professional Certificate program. You will have the opportunity to gain in-demand skills and practice coding your own models, train neural networks, and develop a portfolio to showcase your work to employers.

What is hyperparameter optimization in machine learning?

Hyperparameter optimization, or hyperparameter tuning, is a process for identifying the optimal hyperparameters for your machine learning model through an iterative testing process, where you can adjust the hyperparameter values until you find the best possible balance. The balance you’re trying to achieve is between variance (the ability of a model to process new data) and bias (the ability of a model to connect relationships between data points for accurate predictions). Finding the right amount of each variance and bias is how you establish optimal hyperparameters for your model. 

Fine-tuning your model's hyperparameters alters the function and structure of the model, and, therefore, changes the quality of your results. This means you can experiment with a range of hyperparameter values and learn what your model responds best to. It’s possible to tune hyperparameters manually, but automating the process using a hyperparameter learning algorithm is more efficient.

What is an example of a hyperparameter?

A hyperparameter is a variable that you can adjust to optimize the training of machine learning models, such as the learning rate, the number of neural network nodes, and the number of neural network layers.

Types: Manual and automated hyperparameter optimization

You can perform hyperparameter optimization manually or through automated techniques, such as grid search, random search, and Bayesian optimization. Although automated hyperparameter optimization allows you to explore a wider range of hyperparameters in less time, manual optimization has its own unique benefits as well. 

Manual hyperparameter optimization

During manual hyperparameter optimization, you will set different hyperparameters to test. Because this is a manual process, it’s more time-consuming but allows you to learn how specific adjustments to the hyperparameters impact your model, which is useful for research purposes.

Grid search

After designating a range of values, a grid search selects combinations of these values, working through each possible combination in a specific order until figuring out which is best. Because this method tests every combination, it can require a significant amount of time and consume a substantial amount of computational resources, but ultimately delivers the optimal hyperparameter values.

Random search

Rather than working through a predefined list of hyperparameter values like the grid search method, random search randomly selects values to test from a range. This method is comparatively less computationally intensive and more efficient, making it optimal for the initial stages of hyperparameter optimization. However, a random search isn’t a guarantee for finding the most optimal values. Instead, it provides a smaller range in which the optimal value exists.

Bayesian hyperparameter optimization

Bayesian optimization learns from previous tests to more efficiently find the optimal hyperparameter values through regression analysis. After testing hyperparameter values, the Bayesian optimization method selects new values closer to the ideal outcome, gradually getting closer with each combination. This allows Bayesian optimization for hyperparameter tuning to identify the optimal values in as few steps as possible. 

Learn more: What Are Neural Network Parameters?

Who uses hyperparameter optimization, and why?

Data scientists and machine learning engineers, including natural language processing (NLP) engineers, use hyperparameter optimization to improve the training and performance of machine learning models. Through hyperparameter optimization, you can make adjustments to the model to achieve the desired results. The fine-tuning that occurs during hyperparameter optimization is fundamental, with every model requiring a unique combination of values, which you can only discover by testing out different hyperparameters through manual optimization or techniques like grid search, random search, or Bayesian optimization.

How to optimize hyperparameters

To optimize hyperparameters, you need to monitor the results of your testing and perform statistical analysis to identify the optimal combination. It’s also important to choose a predetermined metric that you’re striving to optimize through hyperparameter tuning. For example, if your goal is to reduce variance in your model, you should identify hyperparameters that enable your model to handle new data well. 

Hyperparameter optimization Python

With Python, you can access libraries, including scikit-learn, Optuna, and HyperOpt, to support your hyperparameter optimization process. Python has a reputation as one of the best programming languages you can learn for machine learning.

Getting started in hyperparameter optimization

To begin learning hyperparameter optimization and the growing field of machine learning, focus your efforts on gaining the most in-demand skills by employers. These skills include programming languages like Python and Ruby, experience using machine learning libraries and frameworks like scikit- learn and PyTorch, and the ability to process large amounts of data. Beyond these technical skills, workplace skills such as collaboration and creative thinking can help you take the next step as well. With growth projections anticipating the global machine learning market will increase from $55.80 billion in 2024 to $232.13 billion in 2030, now is a great time to consider a career in this industry [1].

Explore our free machine learning resources

Interested in learning more about machine learning concepts? Explore some of our free resources to keep up with the latest developments, including our LinkedIn newsletter, Career Chat, and the following:

• Watch on YouTube: Career Spotlight: Machine Learning Engineer

• Hear from an expert: 6 Questions with an IBM Data Scientist and AI Engineer

• Take the quiz: Which Python Course Should You Take? Find Out in 1 Minute 

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