Okay, I need to create a Wikipedia-style document about hyperparameters in machine learning under the given categories. Let me start by understanding the requirements.

First, the structure: H1 for the title, an overview section, several H2 and H3 sections, and maybe references or related documents. The content needs to be accurate, objective, detailed enough, easy to read, with explanations of technical terms. Also, use markdown properly with headings, lists, tables, code blocks if needed. The document should be at least 800 characters long.

The title is "하이퍼파라메터" under the categories Science > Artificial Intelligence > Machine Learning. So I need to make sure all sections are relevant to hyperparameters in machine learning.

Starting with the H1: "하이퍼파라메터". Then an introduction explaining what hyperparameters are, their role in ML models, and why they're important. Maybe mention that they are different from model parameters.

Next, sections. Let's think about possible subsections. Maybe:

• Definition and Concept (H2)
• What is a Hyperparameter? (H3)

• Difference between Parameters and Hyperparameters (H3)

• Common Types of Hyperparameters (H2)

• Learning Rate
• Batch Size
• Number of Layers/Neurons
• Regularization Parameters

• Optimization Algorithm Settings

• Importance in Model Performance (H2)

• Impact on Training Speed and Accuracy

• Overfitting vs. Underfitting

• Techniques for Hyperparameter Tuning (H2)

• Grid Search
• Random Search
• Bayesian Optimization

• Automated Methods like AutoML

• Challenges and Best Practices (H2)

• Computational Cost
• Trade-offs in Tuning
• Practical Tips

Then a references or related documents section. Maybe include some key papers, libraries, or further reading.

Now, for each section, I need to ensure the content is accurate. For example, explaining that hyperparameters are set before training and not learned during the process. Differentiating from model parameters like weights in neural networks.

In the common types, list examples with brief explanations. For instance, learning rate controls how much the model updates its weights, batch size affects training speed and memory usage, etc.

Importance section should discuss how choosing the right hyperparameters can lead to better performance, faster convergence, avoiding overfitting or underfitting.

Techniques for tuning: explain each method briefly, maybe mention their pros and cons. For example, grid search is exhaustive but computationally expensive, random search is more efficient but less systematic, Bayesian optimization uses probabilistic models to find optimal points.

Challenges could include the high computational cost of hyperparameter tuning, especially with large models. Best practices might involve starting with default values, using cross-validation, and leveraging automated tools.

I should also check for technical terms that need explanation. For example, "Bayesian optimization" can be briefly explained as a method that uses probability to find optimal parameters efficiently.

Make sure the language is clear and not too jargon-heavy. Avoid markdown in the text except for headings, lists, tables if needed. Maybe include a table comparing different hyperparameter tuning methods.

Check the length: 800 characters is about 150-200 words. The entire document should be longer than that. Need to elaborate each section sufficiently.

Also, ensure that all information is accurate. For example, confirm that batch size affects both training speed and model performance, and that regularization parameters like L1/L2 in SVMs or dropout rates in neural networks are indeed hyperparameters.

Avoid any subjective statements; stick to objective facts. Use examples where possible, like mentioning specific algorithms (e.g., SGD with learning rate, decision trees with max depth).

Finally, review the structure for logical flow and completeness. Make sure each section adds value and covers key aspects of hyperparameters in machine learning.