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- 2024년 11월 14일 (목) 04:35 데이터베이스 제1정규형 (역사 | 편집) [2,638 바이트] 핵톤 (토론 | 기여) (Created page with "'''제1정규형'''(First Normal Form, 1NF)은 데이터베이스 정규화의 첫 번째 단계로, 테이블의 모든 속성이 원자값(atomic value)을 가지도록 설계하는 것을 의미한다. 즉, 테이블 내의 각 열(속성)은 더 이상 나눌 수 없는 단일 값을 가져야 한다. 이를 통해 데이터의 중복을 줄이고 데이터 무결성을 강화할 수 있다. ==제1정규형의 조건== 제1정규형을 만족하기 위해서는 다...") 태그: 시각 편집
- 2024년 11월 14일 (목) 01:39 Apache AllowOverride (역사 | 편집) [4,080 바이트] Prairie (토론 | 기여) (Created page with "The '''AllowOverride''' directive in Apache HTTP Server is used to specify which types of directives can be overridden by `.htaccess` files in specific directories. By default, Apache uses configuration files like `httpd.conf` or `apache2.conf` for global settings, but `AllowOverride` enables web administrators to override these settings at the directory level using `.htaccess` files. This is particularly useful for shared hosting environments where users may need to man...") 태그: 시각 편집
- 2024년 11월 14일 (목) 01:37 Apache Require (역사 | 편집) [3,845 바이트] Prairie (토론 | 기여) (Created page with "The '''Require''' directive in Apache HTTP Server is used to control access to resources by specifying conditions that clients must meet to be granted access. The `Require` directive is commonly used for user authentication, IP-based access control, and group-based restrictions, enhancing the security and flexibility of web applications. ==Purpose of Require== The '''Require''' directive enables fine-grained access control by setting specific conditions. This can be usef...") 태그: 시각 편집
- 2024년 11월 14일 (목) 01:34 Apache AddType (역사 | 편집) [3,243 바이트] Prairie (토론 | 기여) (Created page with "The '''AddType''' directive in Apache HTTP Server is used to define or change the MIME (Multipurpose Internet Mail Extensions) type for specific file extensions. MIME types tell the browser how to handle files received from the server, such as rendering HTML, displaying images, or executing scripts. Setting the correct MIME type is essential for the server to communicate file handling instructions to the client. ==Purpose of AddType== The '''AddType''' directive helps in...") 태그: 시각 편집
- 2024년 11월 14일 (목) 01:12 Apache Options MultiViews (역사 | 편집) [3,061 바이트] Prairie (토론 | 기여) (Created page with "The '''Options Multiviews''' directive in Apache HTTP Server allows content negotiation by enabling the server to automatically select the best-matching file based on the client’s request. When enabled, the `Multiviews` option allows Apache to match and serve files with various extensions without requiring the full file name in the URL, improving flexibility in file handling and localization. ==Purpose of Options Multiviews== The '''Options Multiviews''' directive help...") 태그: 시각 편집
- 2024년 11월 14일 (목) 01:11 Apache Options Indexes (역사 | 편집) [2,289 바이트] Prairie (토론 | 기여) (Created page with "The '''Options Indexes''' directive in Apache HTTP Server configures the display of directory listings. When enabled, this option allows users to see a list of files in a directory if no default file (like `index.html` or `index.php`) is present. This can be useful for browsing available files, but it also presents security considerations, as it can expose sensitive information. ==Purpose of Options Indexes== The '''Options Indexes''' directive controls whether Apache wi...") 태그: 시각 편집
- 2024년 11월 13일 (수) 12:15 TCP 왕복 시간 (역사 | 편집) [3,888 바이트] Prairie (토론 | 기여) (Created page with "'''TCP 왕복시간'''(TCP RTT: Round Trip Time)는 TCP 연결에서 패킷이 송신된 후 수신자로부터 응답(ACK)을 받는 데 걸리는 시간을 의미한다. RTT는 네트워크 지연을 측정하는 중요한 요소로, TCP가 최적의 데이터 전송 속도를 유지하고, 패킷 손실을 최소화하는 데 필수적인 정보이다. TCP RTT는 네트워크 품질, 대역폭, 지연 요소에 따라 달라지며, 이를 통해 네트워크 혼잡을...") 태그: 시각 편집
- 2024년 11월 13일 (수) 12:02 TIME WAIT 상태 (역사 | 편집) [3,552 바이트] Prairie (토론 | 기여) (Created page with "'''Time Wait 상태'''는 TCP 연결이 종료된 후, 해당 연결의 포트 번호가 재사용되기 전까지 일정 시간 동안 유지되는 상태를 의미한다. 이 상태는 TCP/IP 프로토콜에서의 연결 종료 과정을 안전하게 마무리하고, 패킷 재전송으로 인한 문제를 방지하기 위해 사용된다. ==개요== TCP 연결은 송신자와 수신자가 모두 연결을 종료하는 과정을 거치며, 이를 통해 원활하고...") 태그: 시각 편집
- 2024년 11월 13일 (수) 11:26 TIME WAIT state (역사 | 편집) [2,829 바이트] Prairie (토론 | 기여) (Created page with "The '''TIME_WAIT state''' is a crucial phase in the Transmission Control Protocol (TCP) that occurs after a connection has been terminated. This state ensures that all data packets have been properly transmitted and acknowledged, preventing potential issues from delayed packets in the network. ==Purpose of TIME_WAIT== 1. '''Preventing Delayed Packet Issues''': After a connection closes, packets that were delayed in the network might still arrive. The TIME_WAIT state ensu...") 태그: 시각 편집
- 2024년 11월 5일 (화) 09:10 Missing Data (역사 | 편집) [6,433 바이트] 핵톤 (토론 | 기여) (Created page with "Missing Data refers to the absence of values in a dataset, which can occur due to various reasons such as data entry errors, equipment malfunctions, or privacy concerns. Handling missing data is crucial in data science and machine learning, as it can impact the quality, accuracy, and interpretability of models. Properly addressing missing values ensures that analyses are more reliable and that models generalize well to new data. ==Types of Missing Data== There are three...") 태그: 시각 편집
- 2024년 11월 5일 (화) 09:04 Normalization (Data Science) (역사 | 편집) [5,085 바이트] 핵톤 (토론 | 기여) (Created page with "Normalization in data science is a preprocessing technique used to adjust the values of numerical features to a common scale, typically between 0 and 1 or -1 and 1. Normalization ensures that features with different ranges contribute equally to the model, improving training stability and model performance. It is especially important in machine learning algorithms that rely on distance calculations, such as k-nearest neighbors (kNN) and clustering. ==Importance of Normali...") 태그: 시각 편집
- 2024년 11월 5일 (화) 07:49 Bias-Variance Trade-Off (역사 | 편집) [6,277 바이트] 핵톤 (토론 | 기여) (Created page with "The Bias-Variance Trade-Off is a fundamental concept in machine learning that describes the balance between two sources of error that affect model performance: bias and variance. The goal is to achieve a balance between bias and variance that minimizes the model’s total error, enabling it to generalize well to new, unseen data. ==Understanding Bias and Variance== *'''Bias''': Refers to the error introduced by approximating a complex real-world problem with a simplified...") 태그: 시각 편집
- 2024년 11월 5일 (화) 07:10 Decision Tree Prunning (역사 | 편집) [4,593 바이트] 핵톤 (토론 | 기여) (Created page with "Pruning is a technique used in decision trees and machine learning to reduce the complexity of a model by removing sections of the tree that provide little predictive power. The primary goal of pruning is to prevent overfitting, ensuring that the model generalizes well to unseen data. Pruning is widely used in decision trees and ensemble methods, such as random forests, to create simpler, more interpretable models. ==Types of Pruning== There are two main types of pruning...") 태그: 시각 편집
- 2024년 11월 5일 (화) 07:05 N-Fold Cross-Validation (역사 | 편집) [5,458 바이트] 핵톤 (토론 | 기여) (Created page with "N-Fold Cross-Validation is a technique used in machine learning to evaluate a model's performance by dividing the dataset into multiple subsets, or "folds." In this method, the dataset is split into N equal parts, where the model is trained on N-1 folds and tested on the remaining fold. This process is repeated N times, each time using a different fold as the test set, and the results are averaged to obtain an overall performance estimate. N-fold cross-validation helps t...") 태그: 시각 편집
- 2024년 11월 5일 (화) 06:50 Undersampling (역사 | 편집) [5,426 바이트] 핵톤 (토론 | 기여) (Created page with "'''Undersampling is a technique used in data science and machine learning to address class imbalance by reducing the number of samples in the majority class'''. Unlike oversampling, which increases the representation of the minority class, undersampling aims to balance the dataset by removing instances from the majority class. This technique is commonly applied in scenarios where the majority class significantly outnumbers the minority class, such as fraud detection...") 태그: 시각 편집
- 2024년 11월 5일 (화) 06:47 Oversampling (역사 | 편집) [5,524 바이트] 핵톤 (토론 | 기여) (Created page with "Oversampling is a technique used in data science and machine learning to address class imbalance by increasing the number of samples in the minority class. In classification tasks with imbalanced datasets, oversampling helps to balance the distribution of classes, allowing the model to learn patterns from both majority and minority classes. Oversampling is commonly used in applications such as fraud detection, medical diagnosis, and other areas where certain classes are...") 태그: 시각 편집
- 2024년 11월 5일 (화) 06:42 Stratified Sampling (역사 | 편집) [4,799 바이트] 핵톤 (토론 | 기여) (Created page with "Stratified Sampling is a sampling technique used to ensure that subsets of data (called “strata”) maintain the same distribution of key characteristics as the original dataset. In data science and machine learning, stratified sampling is often used to create training, validation, and test splits, particularly when dealing with imbalanced datasets. This method ensures that each subset is representative of the entire dataset, improving the model's ability to generalize...") 태그: 시각 편집
- 2024년 11월 5일 (화) 06:36 Data Partition (역사 | 편집) [5,033 바이트] 핵톤 (토론 | 기여) (Created page with "'''Data Partition is a process in data science and machine learning where a dataset is divided into separate subsets to train, validate, and test a model'''. Data partitioning ensures that the model is evaluated on data it has not seen before, helping prevent overfitting and ensuring that it generalizes well to new data. Common partitions include training, validation, and test sets, each serving a specific purpose in the model development process. ==Types of Data Partiti...") 태그: 시각 편집
- 2024년 11월 5일 (화) 06:16 Feature Selection (역사 | 편집) [5,622 바이트] 핵톤 (토론 | 기여) (Created page with "'''Feature Selection is a process in machine learning and data science that involves identifying and selecting the most relevant features (or variables) in a dataset to improve model performance, reduce overfitting, and decrease computational cost'''. By removing irrelevant or redundant features, feature selection simplifies the model, enhances interpretability, and often improves accuracy. ==Importance of Feature Selection== Feature selection is a crucial step in the mo...") 태그: 시각 편집
- 2024년 11월 5일 (화) 06:06 Information Gain (역사 | 편집) [4,797 바이트] 핵톤 (토론 | 기여) (Created page with "Information Gain is a metric used in machine learning to measure the effectiveness of a feature in classifying data. It quantifies the reduction in entropy (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 for each node split, maximizing the model’s predictive accuracy. ==Definition of Information Gain== Information gain is defined as the difference in entropy...") 태그: 시각 편집
- 2024년 11월 5일 (화) 06:04 Impurity (Data Science) (역사 | 편집) [4,930 바이트] 핵톤 (토론 | 기여) (Created page with "In data science, impurity refers to the degree of heterogeneity in a dataset, specifically within a group of data points. Impurity is commonly used in decision trees to measure how "mixed" the classes are within each node or split. A high impurity indicates a mix of different classes, while a low impurity suggests that the data is homogenous or predominantly from a single class. Impurity measures guide the decision tree-building process by helping identify the best featu...") 태그: 시각 편집
- 2024년 11월 5일 (화) 05:54 Clustering Algorithm (역사 | 편집) [6,606 바이트] 핵톤 (토론 | 기여) (Created page with "Clustering algorithms are a type of unsupervised learning technique used to group similar data points together based on their features. Unlike classification, clustering does not require labeled data, as the goal is to discover inherent structures within the data. Clustering is widely applied in data exploration, customer segmentation, image processing, and anomaly detection. ==Types of Clustering Algorithms== Several types of clustering algorithms are commonly used, eac...") 태그: 시각 편집
- 2024년 11월 5일 (화) 05:50 Gradient Descent (역사 | 편집) [5,617 바이트] 핵톤 (토론 | 기여) (Created page with "'''Gradient Descent''' is an optimization algorithm used to minimize a function by iteratively moving toward the function's minimum. In machine learning, gradient descent is commonly used to minimize the loss function, adjusting model parameters (weights and biases) to improve the model's performance. The algorithm calculates the gradient of the loss function with respect to each parameter and updates the parameters in the opposite direction of the gradient to reduce err...") 태그: 시각 편집
- 2024년 11월 5일 (화) 05:46 Deep Neural Network (역사 | 편집) [7,157 바이트] 핵톤 (토론 | 기여) (Created page with "A Deep Neural Network (DNN) is an artificial neural network with multiple hidden layers between the input and output layers. This deep structure allows the model to learn complex, hierarchical patterns in data by progressively extracting higher-level features from raw inputs. DNNs are foundational to deep learning and have achieved state-of-the-art results in various applications, including image recognition, natural language processing, and robotics. ==Structure of a De...") 태그: 시각 편집
- 2024년 11월 5일 (화) 05:43 Multi-Layer Perceptron (역사 | 편집) [5,660 바이트] 핵톤 (토론 | 기여) (Created page with "A Multi-Layer Perceptron (MLP) is a type of artificial neural network with multiple layers of neurons, including one or more hidden layers between the input and output layers. Unlike single-layer '''perceptrons''', which can only solve linearly separable problems, MLPs can model complex, non-linear relationships, making them suitable for a wide range of machine learning tasks. ==Structure of a Multi-Layer Perceptron== An MLP consists of three main types of...") 태그: 시각 편집
- 2024년 11월 5일 (화) 05:36 Perceptron (역사 | 편집) [4,388 바이트] 핵톤 (토론 | 기여) (Created page with "The Perceptron is a type of artificial neuron and one of the simplest models in machine learning, used for binary classification tasks. It is a linear classifier that learns to separate data into two classes by finding an optimal hyperplane. Originally developed in the 1950s, the perceptron laid the foundation for more complex neural network architectures. ==Structure of a Perceptron== A perceptron consists of several key components: *'''Inputs''': The feature values fro...") 태그: 시각 편집
- 2024년 11월 5일 (화) 05:32 Neural Network (역사 | 편집) [6,649 바이트] 핵톤 (토론 | 기여) (Created page with "A Neural Network is a machine learning model inspired by the structure and functioning of the human brain. Neural networks consist of layers of interconnected nodes, or "neurons," which process data and learn patterns through weighted connections. Neural networks are foundational to deep learning and are used extensively in complex tasks such as image and speech recognition, natural language processing, and robotics. ==Structure of a Neural Network== A typical neural net...") 태그: 시각 편집
- 2024년 11월 5일 (화) 05:29 Machine Learning (역사 | 편집) [6,227 바이트] 핵톤 (토론 | 기여) (Created page with "'''Machine Learning''' is a branch of artificial intelligence (AI) that focuses on building systems that can learn from data, identify patterns, and make decisions with minimal human intervention. By training algorithms on datasets, machine learning enables computers to make predictions, classify data, and detect insights automatically. ==Types of Machine Learning== Machine learning is typically categorized into several types based on the way models learn from data: *'''...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:54 Deep Learning (역사 | 편집) [5,443 바이트] 핵톤 (토론 | 기여) (Created page with "Deep Learning is a subset of machine learning focused on using neural networks with multiple layers to model complex patterns in large datasets. By learning hierarchies of features directly from data, deep learning can automatically extract representations that are often difficult to engineer manually. It is widely used in applications such as image recognition, natural language processing, and autonomous driving. ==Key Concepts in Deep Learning== Deep learning involves...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:53 Similarity (Data Science) (역사 | 편집) [5,243 바이트] 핵톤 (토론 | 기여) (Created page with "In data science, similarity refers to a measure of how alike two data points, items, or sets of features are. It is a fundamental concept in various machine learning and data analysis tasks, particularly in clustering, recommendation systems, and classification. Similarity metrics quantify the closeness or resemblance between data points, enabling models to group, rank, or classify them based on shared characteristics. ==Key Similarity Measures== Several similarity metri...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:28 Cross-Validation (역사 | 편집) [4,467 바이트] 핵톤 (토론 | 기여) (Created page with "Cross-Validation is a technique in machine learning used to evaluate a model’s performance on unseen data. It involves partitioning the dataset into multiple subsets, training the model on some subsets while testing on others. Cross-validation helps detect overfitting and underfitting, ensuring the model generalizes well to new data. ==Key Concepts in Cross-Validation== Cross-validation is based on the following key principles: *'''Training and Validation Splits''': Cr...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:26 Underfitting (역사 | 편집) [4,364 바이트] 핵톤 (토론 | 기여) (Created page with "Underfitting is a common issue in machine learning where a model is too simple to capture the underlying patterns in the data. As a result, the model performs poorly on both training and test datasets, failing to achieve high accuracy. Underfitting occurs when the model lacks the capacity or complexity needed to represent the relationships within the data. ==Causes of Underfitting== Several factors contribute to underfitting in machine learning models: *'''Over-Simplifie...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:25 Overfitting (역사 | 편집) [4,507 바이트] 핵톤 (토론 | 기여) (Created page with "'''Overfitting''' is a common issue in machine learning where a model learns the training data too closely, capturing noise and specific patterns that do not generalize well to new, unseen data. This results in high accuracy on the training set but poor performance on test data, as the model fails to generalize and instead memorizes irrelevant details. ==Causes of Overfitting== Several factors contribute to overfitting in machine learning models: *'''Complex Models''': M...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:22 Unsupervised Learning (역사 | 편집) [4,902 바이트] 핵톤 (토론 | 기여) (Created page with "Unsupervised Learning is a type of machine learning where the model is trained on an unlabeled dataset, meaning the data has no predefined outputs. The goal is for the model to discover hidden patterns, structures, or relationships within the data. Unsupervised learning is widely used for tasks like clustering, dimensionality reduction, and anomaly detection, where understanding the inherent structure of data is valuable. ==Key Concepts in Unsupervised Learning== Several...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:21 Supervised Learning (역사 | 편집) [4,449 바이트] 핵톤 (토론 | 기여) (Created page with "'''Supervised Learning''' is a type of machine learning where the model is trained on a labeled dataset, meaning each input comes with a corresponding output. The goal is to learn a mapping from inputs to outputs, allowing the model to make predictions or classifications based on new, unseen data. Supervised learning is widely used in applications where historical data can be used to predict future outcomes. ==Key Concepts in Supervised Learning== Several key concepts fo...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:20 First Principles Algorithm (역사 | 편집) [5,424 바이트] 핵톤 (토론 | 기여) (Created page with "First Principles Algorithms are computational methods that rely on fundamental principles, such as mathematics, statistics, physics, or other scientific laws, to model and predict outcomes. Unlike empirical or data-driven approaches, these algorithms are based on established principles and are often used to provide interpretable and theoretically sound predictions. Common first principles algorithms include Decision Trees, Naïve Bayes, and k-Nearest Neighbors (kNN), whi...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:15 Learner (Data Science) (역사 | 편집) [4,020 바이트] 핵톤 (토론 | 기여) (Created page with "In data science, a '''learner''' is an algorithm or model that "learns" patterns from data to make predictions or decisions. Often referred to as an '''inducer or induction algorithm''', a learner uses data samples to induce a general model that can predict outcomes on new, unseen data. The learning process involves identifying relationships between features (input variables) and target variables (output), refining the model with each iteration or training cycle. ==Termi...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:07 Rows (Data Science) (역사 | 편집) [3,241 바이트] 핵톤 (토론 | 기여) (Created page with "In data science, a '''row''' represents a single record or observation in a dataset. Rows, often referred to as '''examples, instances, or data points''', contain values for each feature or attribute, capturing one complete set of information in a structured format. Each row is typically analyzed as an individual unit, providing insights that contribute to broader trends or predictions when aggregated with other rows. ==Terminology== Several terms are used interchangeabl...") 태그: 시각 편집
- 2024년 11월 5일 (화) 02:03 Feature (Data Science) (역사 | 편집) [3,321 바이트] 핵톤 (토론 | 기여) (Created page with "In data science, a '''feature''' is an individual measurable property or characteristic of a data point that is used as input to a predictive model. Terms such as '''feature, columns, attributes, variables, and independent variables''' are often used interchangeably to refer to the input characteristics in a dataset that are used for analysis or model training. ==Types of Features== Features can take various forms depending on the type of data and the problem being solve...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:35 Cold Start Problem (역사 | 편집) [3,384 바이트] 핵톤 (토론 | 기여) (Created page with "The Cold Start Problem is a common challenge in recommender systems, where the system struggles to make accurate recommendations due to a lack of sufficient data. This problem affects new users, new items, or entire systems that lack historical data, limiting the effectiveness of collaborative and content-based filtering techniques. ==Types of Cold Start Problems== Cold start issues can occur in several contexts: *'''User Cold Start''': When a new user joins the platform...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:34 Content-Based Filtering (역사 | 편집) [3,108 바이트] 핵톤 (토론 | 기여) (Created page with "Content-Based Filtering is a recommendation technique that suggests items to users based on the characteristics of items they have previously shown interest in. Unlike collaborative filtering, which relies on user behavior patterns, content-based filtering uses item attributes or features to make recommendations. ==How Content-Based Filtering Works== Content-based filtering involves analyzing item attributes and matching them to a user’s preferences or past interaction...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:31 Collaborative Filtering (역사 | 편집) [3,798 바이트] 핵톤 (토론 | 기여) (Created page with "Collaborative Filtering is a popular technique in recommender systems that predicts a user’s interest by identifying patterns from the behavior and preferences of similar users or items. It relies on the assumption that if users have agreed on past items, they are likely to agree on similar items in the future. ==Types of Collaborative Filtering== Collaborative Filtering can be divided into two main approaches: *'''User-Based Collaborative Filtering''': Recommends item...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:29 Recommender System (역사 | 편집) [3,866 바이트] 핵톤 (토론 | 기여) (Created page with "A Recommender System is a data-driven algorithm designed to suggest relevant items or content to users based on their preferences, behavior, or similar users’ choices. It is widely used in e-commerce, streaming services, social media, and other online platforms to enhance user experience by delivering personalized recommendations. ==Types of Recommender Systems== There are several main types of recommender systems, each with different approaches to making recommendatio...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:26 Precision-Recall Curve (역사 | 편집) [3,840 바이트] 핵톤 (토론 | 기여) (Created page with "The Precision-Recall Curve is a graphical representation used in binary classification to evaluate a model's performance, especially in imbalanced datasets. It plots precision (y-axis) against recall (x-axis) at various threshold settings, showing the trade-off between the two metrics as the decision threshold changes. ==What is a Precision-Recall Curve?== A Precision-Recall Curve shows how well a model balances precision (the accuracy of positive predictions) and recall...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:19 Gain Chart (역사 | 편집) [3,862 바이트] 핵톤 (토론 | 기여) (Created page with "A Gain Chart, or Cumulative Gain Chart, is a graphical tool used to evaluate the effectiveness of a predictive model by showing the cumulative percentage of positive outcomes identified as more of the dataset is included. It helps assess how well the model ranks positive cases, particularly in applications where targeting high-value instances is essential. ==What is a Gain Chart?== A Gain Chart plots the cumulative percentage of positive outcomes (y-axis) against the cum...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:19 Cumulative Response Curve (역사 | 편집) [3,296 바이트] 핵톤 (토론 | 기여) (Created page with "'''Cumulative Response Curve (CRC)''' is graphical tools used in predictive modeling and data science to assess a model's ability to capture positive outcomes as more of the dataset is selected. They provide insight into how effectively a model identifies the highest value cases early in the ranking. ==What is a Cumulative Response Curve?== A Cumulative Response Curve plots the cumulative percentage of actual positive instances (y-axis) against the cumulative percentage...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:18 Lift Curve (역사 | 편집) [3,328 바이트] 핵톤 (토론 | 기여) (Created page with "A '''Lift Curve''' is a graphical representation used in predictive modeling to measure the effectiveness of a model in identifying positive outcomes, compared to a baseline of random selection. It shows how much more likely the model is to capture positive cases within selected segments compared to a random approach. ==What is a Lift Curve?== A Lift Curve plots the lift (y-axis) against the cumulative percentage of the dataset selected (x-axis). It illustrates how well...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:16 Gain (Data Science) (역사 | 편집) [3,713 바이트] 핵톤 (토론 | 기여) (Created page with "'''Gain''' is a metric used in data science, marketing, and predictive modeling to measure the cumulative success of a model in capturing positive outcomes as more of the dataset is utilized. It provides insight into how effectively a model ranks and selects positive cases, particularly in applications where maximizing the return on targeted resources is essential. ==What is Gain?== Gain quantifies the cumulative proportion of positive outcomes identified by the model as...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:15 Lift (Data Science) (역사 | 편집) [3,303 바이트] 핵톤 (토론 | 기여) (Created page with "'''Lift''' is a metric used in marketing, sales, and data science to measure the effectiveness of a predictive model, especially in identifying positive outcomes such as likely buyers or high-risk customers. It quantifies how much better a model performs in comparison to random chance. ==Understanding Lift== Lift evaluates the concentration of positive instances (e.g., buyers, responders) within a selected group compared to the overall rate of positives in the entire pop...") 태그: 시각 편집
- 2024년 11월 4일 (월) 14:12 Data Science Cheat Sheet (역사 | 편집) [6,117 바이트] 핵톤 (토론 | 기여) (Created page with "== Confusion Matrix and F1 Score == '''Confusion Matrix''' {| class="wikitable" |- ! !!Predicted Positive!!Predicted Negative |- |'''Actual Positive'''||True Positive (TP)||False Negative (FN) |- |'''Actual Negative'''||False Positive (FP)||True Negative (TN) |} '''F1 Score''' = 2 * (Precision * Recall) / (Precision + Recall) * 2 * (Positive Predictive Value * True Positive Rate) / (Positive Predictive Value + True Positive Rate) * 2 * (TP) / (TP + FP + FN) ==...") 태그: 시각 편집
