Okay, I need to create a professional Korean wiki-style document about "학습 데이터 (Training Data) under the categories of Technology, Data Science and Analysis. The user provided specific structure and content requirements. Let me by breaking down the taskFirst, the title should be H1, so I'll use "# 학습 데이터". Then the overview section. I should explain what training data is in simple terms, maybe mention its role in machine learning. Next, the detailed sections. The user mentioned H2 and H3 headings. Let me think of possible sub-sections.

Possible H2 sections could be: - 구성 요소 (Components) - 수집 및 준비 과정 (Collection and Preparation) - 중요성 (Importance) - 품질 평가 기준 (Quality Evaluation Criteria) - 관련 개념 (Related Concepts)

For each H2, I need H3s if necessary. For example, under 구성 요소, I can list 데이터 샘플, 라벨, 메타데이터. In 수집 및 준비, steps like 데이터 수집, 전처리, 분할 might be good. Also, maybe include ethical considerations here.

In the importance section, I should talk about model performance, generalization, and maybe overfitting. Quality evaluation could have accuracy, 다양성, 대표성, 균형. Related concepts might include validation/test data, data augmentation, etc.

I need to ensure the content is accurate and objective. Use technical terms but explain them. For example, when mentioning overfitting, briefly define it. Also, include markdown elements like lists and tables where appropriate. Maybe a table for quality evaluation criteria.

I should check the length. 800 characters minimum, but the example response is longer, so I need to make sure there's enough depth. Also, include a 참고 자료 section at the end with some sources.

Wait, the user provided an example response. Let me check that to see if I missed any sections. They have 개요, 구성 요소, 수집 및 준비 과정, 중요성, 품질 평가 기준, 관련 문서. So I should follow that structure.

I need to make sure each section flows well. Use bullet points and subheadings. Maybe add some emphasis with bold or italics. Also, code blocks if there's any technical code, but since the topic is training data in general, maybe not necessary unless giving an example of data format.

Also, ensure that the language is professional but easy to understand. Avoid overly complex sentences. Check for any technical terms that need explanation. For example, when talking about 데이터 전처리, mention normalization or handling missing values.

Let me start drafting each section. The overview should introduce training data as the foundation of ML models, used to train algorithms. Then components: samples, labels, metadata. Explain each briefly.

In collection and preparation: sources like public datasets, APIs, manual entry. Preprocessing steps: cleaning, normalization, augmentation. Data splitting into train, validation, test.

Importance section: how quality data affects model accuracy, generalization. Mention overfitting and underfitting here. Quality evaluation: accuracy, diversity, representativeness, balance. Maybe a table here.

Related concepts could link to validation data, test data, data augmentation techniques. References section with some authoritative sources like books or articles.

I should also check for any possible errors. For example, ensuring that the difference between training, validation, and test data is clear. Also, that the data preparation steps are accurately described.

Finally, ensure the markdown syntax is correct: headers with #, subheaders with ## and ###, lists with -, tables formatted properly. Avoid any markdown that might not render well.