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• Main Page
  ...ute immediately. Please add or edit articles on topics related to Computer Science and its subfields right now. Your contributions benefit everyone. This wiki aims to provide deeper coverage of Computer Science topics compared to Wikipedia.
  2 KB (233 단어) - 2025년 1월 31일 (금) 05:27
• Information Gain
  ...impurity]]) achieved by splitting a dataset based on a particular feature. Information gain is widely used in decision tree algorithms to select the best feature ==Definition of Information Gain==
  5 KB (671 단어) - 2024년 11월 5일 (화) 06:11
• Entropy (Data Science)
  ...mples. Entropy is a fundamental concept for calculating [[Information Gain|information gain]], helping guide the tree-building process by choosing splits that red ...uncertainty in a dataset based on class distributions. It originates from information theory and is commonly used in decision tree algorithms.
  6 KB (804 단어) - 2024년 11월 5일 (화) 06:14
• Impurity (Data Science)
  In data science, impurity refers to the degree of heterogeneity in a dataset, specifically *'''Entropy''': A metric from information theory, entropy measures the level of disorder or unpredictability in the d
  5 KB (677 단어) - 2024년 11월 5일 (화) 06:04
• Ontology
  '''Ontology''' in computer science and information science refers to a formal representation of knowledge within a specific domain. It [[분류:Information Science]]
  3 KB (369 단어) - 2024년 12월 2일 (월) 06:13
• Leakage (Data Science)
  ...ng model evaluation, as the model effectively "cheats" by having access to information it would not have in a real-world application. Leakage is a critical issue **Occurs when information that would not normally be available at prediction time is included in the
  5 KB (730 단어) - 2024년 12월 1일 (일) 09:20
• Rows (Data Science)
  ...ontain values for each feature or attribute, capturing one complete set of information in a structured format. Each row is typically analyzed as an individual uni Several terms are used interchangeably with "row" in data science:
  3 KB (454 단어) - 2024년 11월 5일 (화) 02:07
• Feature Selection
  '''Feature Selection is a process in machine learning and data science that involves identifying and selecting the most relevant features (or vari **Examples: Correlation, Chi-Squared Test, ANOVA F-test, and Mutual Information.
  5 KB (692 단어) - 2024년 11월 5일 (화) 08:09
• Dimensionality Reduction
  ...er of features (dimensions) in a dataset while preserving as much relevant information as possible. It simplifies data visualization, reduces computational costs, *'''Loss of Information:''' Some relevant information may be lost during the dimensionality reduction process.
  4 KB (464 단어) - 2024년 12월 2일 (월) 06:09
• Undersampling
  '''Undersampling is a technique used in data science and machine learning to address class imbalance by reducing the number of s ...of Information Loss''': Randomly removing samples may eliminate important information, potentially reducing model accuracy.
  5 KB (720 단어) - 2024년 11월 5일 (화) 06:50
• Collaborative Filtering
  *'''Prediction Generation''': Using the neighborhood information, the system predicts a user’s interest in an item by aggregating preferen *'''No Need for Item Metadata''': Does not require detailed information about items, relying solely on user interactions.
  4 KB (490 단어) - 2024년 11월 4일 (월) 14:33
• Principal Component Analysis
  ...data, helping to reduce the number of features while preserving essential information. ...ality Reduction:''' Simplifies complex datasets while preserving essential information.
  4 KB (485 단어) - 2024년 12월 2일 (월) 06:21
• False Positive Rate
  *It does not provide information about the model’s performance on the positive class (true positives), so [[Category:Data Science]]
  2 KB (309 단어) - 2024년 11월 4일 (월) 13:57
• Independence (Linear Regression)
  ...it implies that the value of one observation does not influence or provide information about another observation. [[Category:Data Science]]
  2 KB (318 단어) - 2024년 11월 4일 (월) 11:18
• Outlier (Data Science)
  *'''Environmental Science:''' Spotting unusual environmental readings, such as temperature spikes. ...Information Loss:''' Removing valid outliers may lead to loss of critical information.
  4 KB (571 단어) - 2024년 12월 2일 (월) 13:11
• Missing Data
  ...alfunctions, or privacy concerns. Handling missing data is crucial in data science and machine learning, as it can impact the quality, accuracy, and interpret ...issing. This approach retains all data while also providing the model with information about the missingness pattern.
  6 KB (909 단어) - 2024년 11월 30일 (토) 15:22
• First Principles Algorithm
  ...l common first principles algorithms are used in machine learning and data science: ...atures, splitting data according to conditions derived from basic rules of information gain or Gini impurity.
  5 KB (691 단어) - 2024년 11월 5일 (화) 02:20
• Precision-Recall Curve
  *'''Focuses Only on Positive Class''': Precision-Recall Curves don’t provide information about negative class performance, which may be relevant in some application [[Category:Data Science]]
  4 KB (504 단어) - 2024년 11월 4일 (월) 14:26
• Decision Tree
  ...tifies data disorder, where a decrease in entropy signifies an increase in information gain. [[Category:Data Science]]
  3 KB (454 단어) - 2024년 11월 4일 (월) 11:43
• Recommender System
  *'''User Data''': Information on user activity, preferences, purchase history, and ratings. [[Category:Data Science]]
  4 KB (486 단어) - 2024년 11월 4일 (월) 14:33

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