Hyperparameter tuning and optimization are essential steps in building and improving predictive models. But how do you learn from your trials and errors, and use them to enhance your future performance? In this article, we will share some tips and best practices to help you analyze, document, and apply your hyperparameter tuning and optimization results.
Before you start tuning and optimizing your hyperparameters, you need to have a clear idea of what you want to achieve. What is your objective function, or the metric that you want to optimize? What are the constraints or trade-offs that you need to consider, such as computational resources, time, or complexity? How will you measure and compare your results, and what are the baselines or benchmarks that you want to beat? Having a well-defined goal will help you focus your efforts and avoid unnecessary or irrelevant experiments.
The new frontier of hyperparameters tuning is automatic optimization-based tuning. Tools such as optuna allow to specify a range of parameters and allow a model to minimize a specific loss function, e.g. RMSE, while going through the search space.
There are many methods and tools available for hyperparameter tuning and optimization, such as grid search, random search, Bayesian optimization, evolutionary algorithms, or automated machine learning. Each method has its own advantages and disadvantages, depending on the problem, the data, and the model. You need to choose the method that suits your needs and preferences, and understand how it works, what assumptions it makes, and what parameters it requires. You also need to decide how to split your data into training, validation, and test sets, and how to handle cross-validation or nested validation.
As you run your experiments, you need to keep track of your hyperparameter settings, your objective function values, and any other relevant information, such as errors, warnings, or logs. You can use tools such as MLflow, TensorBoard, or Weights & Biases to record and visualize your experiments, or create your own spreadsheet or notebook. Tracking your experiments will help you analyze your results, identify patterns or trends, and spot any anomalies or outliers.
Once you have completed your experiments, you need to analyze your results and draw conclusions. You need to answer questions such as: Which hyperparameters had the most impact on your objective function? Which combinations of hyperparameters performed the best or the worst? How did your results vary across different data splits or folds? How did your results compare to your baselines or benchmarks? How did your results match your expectations or hypotheses? You can use tools such as pandas, matplotlib, or seaborn to explore and visualize your data, or use statistical tests or metrics to quantify your findings.
After you have analyzed your results, you need to document your findings and share them with others. You need to write a clear and concise report that summarizes your goal, your method, your experiments, and your conclusions. You need to include tables, charts, or graphs that illustrate your results, and explain how you interpreted them. You need to highlight the key insights or takeaways that you learned from your hyperparameter tuning and optimization process, and how they can inform your future decisions or actions.
Finally, you need to apply your learnings and use them to improve your predictive models. You need to select the best hyperparameter settings for your model, and test it on new or unseen data. You need to evaluate your model's performance, and compare it to other models or methods. You need to identify any gaps or limitations that your model still has, and brainstorm ideas or solutions to overcome them. You need to update your code, documentation, or repository with your latest changes, and communicate your results and recommendations to your stakeholders or clients.