User contributions for Dendrogram

1 December 2024

• 23:1823:18, 1 December 2024 diff hist +4,378‎ N Observational Machine Learning Method ‎ 새 문서: '''Observational Machine Learning Methods''' are techniques designed to analyze data collected from observational studies rather than controlled experiments. In such studies, the assignment of treatments or interventions is not randomized, which can introduce biases and confounding factors. Observational ML methods aim to identify patterns, relationships, and causal effects within these datasets. ==Key Challenges in Observational Data== Observational data often comes with inhere... current Tag: Visual edit
• 23:1323:13, 1 December 2024 diff hist +4,262‎ N Propensity Score Matching ‎ 새 문서: '''Propensity Score Matching (PSM)''' is a statistical technique used in observational studies to reduce selection bias when estimating the causal effect of a treatment or intervention. It involves pairing treated and untreated units with similar propensity scores, which represent the probability of receiving the treatment based on observed covariates. ==Key Concepts== *'''Propensity Score:''' The probability of a unit receiving the treatment, given its covariates. *'''Matching:... current Tag: Visual edit
• 23:1323:13, 1 December 2024 diff hist +3,695‎ N Causal Graph ‎ 새 문서: '''Causal Graph''' is a directed graph used to represent causal relationships between variables in a dataset. Each node in the graph represents a variable, and directed edges (arrows) indicate causal influence from one variable to another. Causal graphs are widely used in causal inference, machine learning, and decision-making processes. ==Key Components of a Causal Graph== A causal graph typically consists of the following: *'''Nodes:''' Represent variables in the system (e.g.,... current Tag: Visual edit
• 23:1223:12, 1 December 2024 diff hist +1,947‎ N Data Science Contents ‎ 새 문서: === 1. Understanding Data Science === * What is Data Science? * Impact on Business * Key Technologies in Data Science === 2. Data Preparation and Preprocessing === * Data Collection * Handling '''Missing Data''' and '''Outlier'''s * Normalization and Standardization === 3. Exploratory Data Analysis (EDA) === * Goals of Data Analysis * Basic Statistical Analysis * Importance of Data Visualization === 4. Supervised Learning === *... current Tag: Visual edit
• 23:1123:11, 1 December 2024 diff hist +4,314‎ N Outlier (Data Science) ‎ 새 문서: '''Outlier''' refers to a data point that significantly deviates from other observations in a dataset. Outliers can arise due to variability in the data, errors in measurement, or rare events. Identifying and addressing outliers is critical in data preprocessing, as they can influence statistical analyses and machine learning models. ==Characteristics of Outliers== Outliers exhibit the following traits: *'''Deviation from Patterns:''' They do not conform to the general distribut... current Tag: Visual edit
• 23:1123:11, 1 December 2024 diff hist −4,298‎ Outlier (Data) ‎ Outlier (Data Science) 문서로 넘겨주기 current Tag: New redirect
• 21:2521:25, 1 December 2024 diff hist +4,338‎ N Outlier (Data) ‎ 새 문서: '''Outlier''' refers to a data point that significantly deviates from other observations in a dataset. Outliers can arise due to variability in the data, errors in measurement, or rare events. Identifying and addressing outliers is critical in data preprocessing, as they can influence statistical analyses and machine learning models. ==Characteristics of Outliers== Outliers exhibit the following traits: *'''Deviation from Patterns:''' They do not conform to the general distribut... Tag: Visual edit
• 16:2116:21, 1 December 2024 diff hist +3,829‎ N Principal Component Analysis ‎ 새 문서: '''Principal Component Analysis (PCA)''' is a statistical technique used for dimensionality reduction by transforming a dataset into a new coordinate system. The transformation emphasizes the directions (principal components) that maximize the variance in the data, helping to reduce the number of features while preserving essential information. ==Key Concepts== *'''Principal Components:''' New orthogonal axes computed as linear combinations of the original features. The first pr... current Tag: Visual edit
• 16:1916:19, 1 December 2024 diff hist +2,936‎ N Singular Value Decomposition ‎ 새 문서: '''Singular Value Decomposition (SVD)''' is a mathematical technique used to decompose a matrix into three component matrices. It is widely used in data analysis, dimensionality reduction, machine learning, and signal processing. ==Definition== SVD decomposes a matrix \( A \) into three matrices: *'''U:''' An orthogonal matrix containing the left singular vectors. *'''Σ (Sigma):''' A diagonal matrix with singular values sorted in descending order. *'''V^T:''' An orthogonal matr... current Tag: Visual edit
• 16:1316:13, 1 December 2024 diff hist +31‎ Ontology ‎No edit summary current Tag: Visual edit
• 16:1316:13, 1 December 2024 diff hist +3,009‎ N Ontology ‎ 새 문서: '''Ontology''' in computer science and information science refers to a formal representation of knowledge within a specific domain. It defines concepts, relationships, and categories to facilitate reasoning, data integration, and knowledge sharing. ==Key Components of an Ontology== An ontology typically consists of the following elements: *'''Classes (Concepts):''' Represent the entities or objects in the domain. *'''Relationships:''' Define how classes are connected (e.g., "is-... Tag: Visual edit
• 16:0916:09, 1 December 2024 diff hist +3,754‎ N Dimensionality Reduction ‎ 새 문서: '''Dimensionality Reduction''' is a technique used in machine learning and data analysis to reduce the number of features (dimensions) in a dataset while preserving as much relevant information as possible. It simplifies data visualization, reduces computational costs, and helps mitigate the curse of dimensionality. ==Importance of Dimensionality Reduction== Dimensionality reduction is crucial for the following reasons: *'''Improves Model Performance:''' Reducing irrelevant or r... current Tag: Visual edit
• 16:0516:05, 1 December 2024 diff hist +3,703‎ N Hash Function ‎ 새 문서: '''Hash Function''' is a mathematical function that transforms input data of arbitrary size into a fixed-length output, called a hash or digest. Hash functions are widely used in computer science, cryptography, and data management for tasks like data integrity, indexing, and secure storage. ==Characteristics of a Hash Function== A good hash function typically satisfies the following properties: *'''Deterministic:''' The same input always produces the same hash. *'''Fast Computat... current Tag: Visual edit
• 15:4515:45, 1 December 2024 diff hist +70‎ Dendrogram ‎No edit summary current Tag: Visual edit
• 15:4415:44, 1 December 2024 diff hist +3,011‎ N Dendrogram ‎ 새 문서: '''Dendrogram''' is a tree-like diagram used to represent the hierarchical relationships among a set of data points. It is commonly used in hierarchical clustering to visualize the order and structure of clusters as they are merged or divided. The height of each branch in a dendrogram indicates the distance or dissimilarity between clusters. ==Structure of a Dendrogram== A dendrogram consists of the following components: *'''Leaves:''' Represent individual data points or initial... Tag: Visual edit
• 15:4315:43, 1 December 2024 diff hist +3,367‎ N Hierarchical Clustering ‎ 새 문서: '''Hierarchical Clustering''' is a clustering method in machine learning and statistics that builds a hierarchy of clusters by either merging smaller clusters into larger ones (agglomerative) or dividing larger clusters into smaller ones (divisive). It is widely used for exploratory data analysis and in domains such as bioinformatics, marketing, and social network analysis. ==Types of Hierarchical Clustering== Hierarchical clustering is divided into two main types: *'''Agglomera... current Tag: Visual edit
• 15:4015:40, 1 December 2024 diff hist +2,884‎ N K-Means++ ‎ 새 문서: '''K-Means++''' is an enhanced initialization algorithm for the K-Means clustering method. It aims to improve the selection of initial cluster centroids, which is a critical step in the K-Means algorithm. By carefully choosing starting centroids, K-Means++ reduces the chances of poor clustering outcomes and accelerates convergence. ==How K-Means++ Works== K-Means++ modifies the standard K-Means initialization by ensuring that the initial centroids are chosen in a way that they a... current Tag: Visual edit
• 15:0215:02, 1 December 2024 diff hist +3,918‎ N K-Means ‎ 새 문서: '''K-Means''' is one of the most popular unsupervised machine learning algorithms used for clustering data into distinct groups. The algorithm partitions a dataset into '''k''' clusters, where each data point belongs to the cluster with the nearest mean. It is widely used for data analysis, pattern recognition, and feature engineering. ==How K-Means Works== The K-Means algorithm follows an iterative process to assign data points to clusters and optimize the cluster centroids: #I... current Tag: Visual edit