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- 06:44, 21 November 2024 Iron Curtain (hist | edit) [2,385 bytes] Deposition (talk | contribs) (Created page with "The '''Iron Curtain''' refers to the political, military, and ideological barrier erected by the Soviet Union after World War II to separate itself and its satellite states in Eastern Europe from the Western world. The term symbolizes the division between communist and non-communist countries during the Cold War. thumb|Iron Curtain === Origins of the Term === The phrase "Iron Curtain" became popular after it was used by British Prime Minister '...") Tag: Visual edit
- 04:04, 21 November 2024 Finite State Machine (hist | edit) [3,023 bytes] Deposition (talk | contribs) (Created page with "A '''Finite State Machine''' (FSM) is a computational model used to design and analyze the behavior of systems. FSMs are characterized by a finite number of states, transitions between those states, and actions that result from those transitions. ==Overview== A finite state machine consists of: *A finite set of states. *A finite set of inputs. *A transition function that determines the next state for a given state and input. *An initial state. *(Optionally) a set of fina...") Tag: Visual edit
- 13:44, 14 November 2024 TCP 혼잡 제어 (hist | edit) [3,175 bytes] Prairie (talk | contribs) (Created page with "'''TCP 혼잡 제어'''(TCP Congestion Control)는 TCP(Transmission Control Protocol)에서 네트워크 혼잡을 관리하고 데이터 손실을 줄이기 위한 메커니즘이다. 혼잡 제어는 네트워크 상태에 따라 전송 속도를 동적으로 조절하여 네트워크 자원의 효율성을 높이고, 혼잡으로 인한 성능 저하를 방지하는 데 중요한 역할을 한다. ==혼잡 제어 알고리즘의 주요 단계== '''혼잡 회피'''(Con...") Tag: Visual edit
- 11:39, 14 November 2024 TCP 시퀀스 번호 (hist | edit) [2,760 bytes] Prairie (talk | contribs) (Created page with "'''TCP 시퀀스 번호'''(TCP Sequence Number)는 TCP(Transmission Control Protocol)에서 데이터 패킷의 순서를 추적하고, 전송 중 손실된 데이터의 재전송 및 올바른 데이터 조립을 보장하기 위해 사용하는 숫자이다. 시퀀스 번호는 TCP 연결에서 매우 중요한 역할을 하며, 송신 측에서 전송하는 각 바이트에 고유한 번호를 할당한다. 수신 측에서는 이를 기반으로 패킷이 올바른...") Tag: Visual edit
- 05:30, 14 November 2024 데이터베이스 후보 키 (hist | edit) [2,823 bytes] 핵톤 (talk | contribs) (Created page with "'''후보 키'''(Candidate Key)는 데이터베이스 테이블에서 각 행을 고유하게 식별할 수 있는 속성 또는 속성들의 집합을 의미한다. 후보 키는 테이블 내의 모든 행을 유일하게 구분할 수 있는 최소한의 속성 집합으로, 기본 키(primary key)로 선택될 수 있는 후보가 된다. ==후보 키의 조건== 후보 키가 되기 위해서는 다음 조건을 만족해야 한다. *'''유일성'''(Uniqueness): 후...") Tag: Visual edit
- 05:28, 14 November 2024 데이터베이스 보이스-코드 정규형 (hist | edit) [3,302 bytes] 핵톤 (talk | contribs) (Created page with "'''보이스-코드 정규형'''(Boyce-Codd Normal Form, BCNF)은 데이터베이스 정규화의 네 번째 단계로, 제3정규형(3NF)을 강화한 형태이다. 보이스-코드 정규형은 제3정규형을 만족하면서, 모든 결정자가 후보 키가 되도록 요구하여 데이터베이스의 설계를 더욱 엄격하게 한다. ==보이스-코드 정규형의 조건== 보이스-코드 정규형을 만족하기 위해서는 다음 조건을 충족해야...") Tag: Visual edit
- 04:55, 14 November 2024 데이터베이스 제3정규형 (hist | edit) [3,346 bytes] 핵톤 (talk | contribs) (Created page with "'''Third Normal Form, 3NF''' '''제3정규형'''은 데이터베이스 정규화의 세 번째 단계로, 제2정규형(2NF)을 만족하면서 테이블 내에서 이행적 종속성(transitive dependency)을 제거하는 것을 목표로 한다. 제3정규형은 기본 키에만 종속하도록 설계하여 데이터 중복을 줄이고 데이터 무결성을 더욱 강화한다. ==제3정규형의 조건== 제3정규형을 만족하기 위해...") Tag: Visual edit
- 04:48, 14 November 2024 부분 함수 종속성 (hist | edit) [2,763 bytes] 핵톤 (talk | contribs) (Created page with "'''Partial Functional Dependency''' '''부분 함수 종속성'''은 데이터베이스 정규화 과정에서, 합성 키(composite key)를 가진 릴레이션에서 기본 키의 일부에만 종속하는 속성이 존재하는 경우를 의미한다. 부분 함수 종속성은 데이터 중복과 비효율적인 데이터 구조를 초래할 수 있으며, 제2정규형(2NF)에서는 이를 제거하는 것이 목표이다. ==개요== 부분 함수 종속성은 주...") Tag: Visual edit
- 04:46, 14 November 2024 데이터베이스 제2정규형 (hist | edit) [3,025 bytes] 핵톤 (talk | contribs) (Created page with "'''Second Normal Form, 2NF''' '''제2정규형'''은 데이터베이스 정규화의 두 번째 단계로, 제1정규형(1NF)을 만족하면서 테이블 내에서 '''부분 함수 종속성(�Partial Functional Dependency)'''을 제거하는 것을 목표로 한다. 제2정규형은 기본 키의 일부에만 종속하는 속성을 제거하여 데이터 중복을 줄이고 데이터 무결성을 향상시킨다. ==제2정규형...") Tag: Visual edit
- 04:35, 14 November 2024 데이터베이스 제1정규형 (hist | edit) [2,638 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 01:39, 14 November 2024 Apache AllowOverride (hist | edit) [4,080 bytes] Prairie (talk | contribs) (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...") Tag: Visual edit
- 01:37, 14 November 2024 Apache Require (hist | edit) [3,845 bytes] Prairie (talk | contribs) (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...") Tag: Visual edit
- 01:34, 14 November 2024 Apache AddType (hist | edit) [3,243 bytes] Prairie (talk | contribs) (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...") Tag: Visual edit
- 01:12, 14 November 2024 Apache Options MultiViews (hist | edit) [3,061 bytes] Prairie (talk | contribs) (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...") Tag: Visual edit
- 01:11, 14 November 2024 Apache Options Indexes (hist | edit) [2,289 bytes] Prairie (talk | contribs) (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...") Tag: Visual edit
- 12:15, 13 November 2024 TCP 왕복 시간 (hist | edit) [3,888 bytes] Prairie (talk | contribs) (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...") Tag: Visual edit
- 12:02, 13 November 2024 TIME WAIT 상태 (hist | edit) [3,552 bytes] Prairie (talk | contribs) (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...") Tag: Visual edit
- 11:26, 13 November 2024 TIME WAIT state (hist | edit) [2,829 bytes] Prairie (talk | contribs) (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...") Tag: Visual edit
- 09:10, 5 November 2024 Missing Data (hist | edit) [5,902 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 09:04, 5 November 2024 Normalization (Data Science) (hist | edit) [5,085 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 07:49, 5 November 2024 Bias-Variance Trade-Off (hist | edit) [6,277 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 07:10, 5 November 2024 Decision Tree Prunning (hist | edit) [4,593 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 07:05, 5 November 2024 N-Fold Cross-Validation (hist | edit) [5,458 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 06:50, 5 November 2024 Undersampling (hist | edit) [5,426 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 06:47, 5 November 2024 Oversampling (hist | edit) [5,524 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 06:42, 5 November 2024 Stratified Sampling (hist | edit) [4,799 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 06:36, 5 November 2024 Data Partition (hist | edit) [5,033 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 06:16, 5 November 2024 Feature Selection (hist | edit) [5,622 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 06:06, 5 November 2024 Information Gain (hist | edit) [4,797 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 06:04, 5 November 2024 Impurity (Data Science) (hist | edit) [4,930 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 05:54, 5 November 2024 Clustering Algorithm (hist | edit) [6,606 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 05:50, 5 November 2024 Gradient Descent (hist | edit) [5,617 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 05:46, 5 November 2024 Deep Neural Network (hist | edit) [7,157 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 05:43, 5 November 2024 Multi-Layer Perceptron (hist | edit) [5,660 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 05:36, 5 November 2024 Perceptron (hist | edit) [4,388 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 05:32, 5 November 2024 Neural Network (hist | edit) [6,649 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 05:29, 5 November 2024 Machine Learning (hist | edit) [6,227 bytes] 핵톤 (talk | contribs) (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: *'''...") Tag: Visual edit
- 02:54, 5 November 2024 Deep Learning (hist | edit) [5,443 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 02:53, 5 November 2024 Similarity (Data Science) (hist | edit) [5,243 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 02:28, 5 November 2024 Cross-Validation (hist | edit) [4,467 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 02:26, 5 November 2024 Underfitting (hist | edit) [4,364 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 02:25, 5 November 2024 Overfitting (hist | edit) [4,507 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 02:22, 5 November 2024 Unsupervised Learning (hist | edit) [4,902 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 02:21, 5 November 2024 Supervised Learning (hist | edit) [4,449 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 02:20, 5 November 2024 First Principles Algorithm (hist | edit) [5,424 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 02:15, 5 November 2024 Learner (Data Science) (hist | edit) [4,020 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 02:07, 5 November 2024 Rows (Data Science) (hist | edit) [3,241 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 02:03, 5 November 2024 Feature (Data Science) (hist | edit) [3,321 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 14:35, 4 November 2024 Cold Start Problem (hist | edit) [3,384 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
- 14:34, 4 November 2024 Content-Based Filtering (hist | edit) [3,108 bytes] 핵톤 (talk | contribs) (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...") Tag: Visual edit
