Hyperparameter Tuning Good Practices for Robust Predictive Models

Want models that actually work?

Data scientists want their models to predict well. After all, high-performing models lead to:

• Smarter decisions

• More accurate forecasts

The problem is…

It's not easy. To develop accurate predictive models, you need to invest the time in proper hyperparameter tuning. Otherwise, models just won't perform.

In this hyperparameter tuning best practices guide, the methods the best professionals in the industry use to build machine learning models that work are covered. This is the same approach you can follow to turn sub-par models into performance powerhouses.

Let's jump right in!

Inside This Guide

• What Is Hyperparameter Tuning?

• Why Hyperparameter Tuning Is Important

• Hyperparameter Tuning Best Practices (5x)

What Is Hyperparameter Tuning?

Hyperparameter tuning is the process of choosing the best settings for a machine learning model.

 

Hyperparameters are the dials on a machine. They control how the model learns from the data. Unlike regular parameters which the model automatically learns during training… Hyperparameters are pre-configured before training starts.

Examples of hyperparameters include:

• Learning rate

• Number of trees in a random forest

• Depth of neural network layers

• Regularization strength

The process of hyperparameter tuning is to find the combination of hyperparameters that make the model perform its best on new data.

Seems important, right?

The good news is that by following the right hyperparameter tuning strategies, your team can dramatically improve the accuracy and reliability of the models it builds. This hyperparameter tuning best practices guide has the exact steps needed to do that.

Why Hyperparameter Tuning Is Important

Hyperparameter tuning is important because it has a big impact on model performance.

Here's the thing…

Machine learning algorithms typically come with a default set of hyperparameters. This is a decent place to start with small, academic problems. But for production-grade machine learning where results matter, most models need their hyperparameters tuned.

For example, in a study published in Political Science Research and Methods, researchers examined machine learning papers and found that just 20% of studies provided any description of their hyperparameters or tuning strategy. That's an outrageously low number.

It's a problem because hyperparameters that aren't properly tuned can lead to:

• Overfitting

• Underfitting

• Wasted computing resources

• Unreliable predictions

Poorly-tuned models and default hyperparameters might work fine in practice sometimes. But they can also lead to wildly suboptimal results.

Building great models is a balancing act. There are too many factors at play for the default settings to get everything "just right."

Think of it like this…

You can drive a car with flat tires. But it's not going to perform anywhere near its potential. Same with machine learning.

5x Hyperparameter Tuning Best Practices

Ok, now to the good stuff. Here are the 5x best practices for building robust predictive models with hyperparameter tuning.

Start with a Baseline Model

Before getting deep into the tuning process, establish a baseline.

This means running the model first with the default hyperparameters. Record the performance metrics. Document the results. This gives you a clear starting point against which to measure improvements.

If you don't have a baseline? There is no way to know whether your tuning efforts are helping.

It's simple but essential.

Choose the Right Search Strategy

There are several strategies for searching the hyperparameter space. Each approach has its strengths.

Grid search is the brute-force method. It exhaustively tests every combination of hyperparameter values. Grid search is 100% thorough but scales horribly when you have many hyperparameters to tune.

Random search randomly samples a fixed number of hyperparameter combinations. It's much faster to run than grid search, and research shows it often finds good solutions more quickly. Random search tends to explore more diverse parts of the search space than grid search.

Bayesian optimization is a more sophisticated search method that uses the results from previous trials to make intelligent guesses about which hyperparameter combinations to try next. According to a study published in the Journal of Electronic Science and Technology, Bayesian optimization found optimal hyperparameters with 26-40% improvement in search speed over random search.

In short: Start with random search to get a feel for the search space. Then use Bayesian optimization to home in on the best hyperparameters.

Focus on the Most Important Hyperparameters

The hyperparameters that have the largest effects on model performance will vary across models and problems. A learning rate is the most important hyperparameter to tune for neural networks. For tree-based methods, max depth and number of estimators tend to have the biggest influence.

In research published in the Journal of Electronic Science and Technology, the authors developed a concept they called "tunability" to characterize how much impact a given hyperparameter had on the results of hyperparameter optimization. They found significant differences in the tunability of different hyperparameters across three different types of models.

Hyperparameters that have little impact on results should be given a lower priority when searching the hyperparameter space. By focusing attention on the highest-impact hyperparameters, you can save time and computing resources.

Use Cross-Validation

Cross-validation is a non-negotiable when tuning model hyperparameters. The basic idea is to split the data into multiple folds. The model is trained on some of the folds and then validated on the others. This process repeats until each fold of data has been used as the validation set.

The benefit of this approach? A single train-test data split can be deceptive. A hyperparameter combination can perform extremely well or poorly just by chance on a specific data split. Cross-validation provides a more honest assessment of how the model will perform on unseen data.

It's standard practice to use k-fold cross-validation with k=5 or k=10. This splits the data into 5 or 10 folds and rotates through them for validation. This provides a good balance between the amount of data used for training and the number of performance estimates collected.

Automate and Document Everything

Hyperparameter tuning can be a tedious and error-prone process if done by hand.

Thankfully, libraries like Optuna, Ray Tune, and Hyperopt make it easy to automate the hyperparameter tuning process. These tools track experiments, parallelize jobs, and implement the search algorithm behind the scenes.

But automation is not enough by itself…

Documentation is critical as well. Record every experiment. Track what hyperparameter combinations were tried and what results were achieved. Make detailed notes.

This makes your results reproducible, which is important for production systems. And a solid knowledge base prevents you from having to repeat failed experiments in the future.

Hyperparameter tuning best practices are designed to build better models more quickly. Teams that put systems and processes in place to automate tuning and document everything go even faster.

Wrapping Things Up

Hyperparameter tuning is what separates models that kinda work from models that provide real value.

Investing time in tuning your models can help teams:

• Save time by avoiding endless manual experimentation

• Conserve resources by focusing on hyperparameters that matter

• Reduce frustration by taking a systematic approach

 

The big takeaways from this hyperparameter tuning best practices guide are:

• Establish a baseline before tuning

• Use the right search strategy for the job

• Focus on high-impact hyperparameters

• Cross-validate everything

• Automate the tuning process and document everything

These practices are the bedrock of predictive model development. The best data science teams in the industry use these methods. They've stood the test of time.

Pick one practice. Get good at it. Then add another. Before long, hyperparameter tuning becomes second nature and your models perform better.

This is the proven path to building machine learning models that actually work in the real world.