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- 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
- 2024년 11월 4일 (월) 14:11 Specificity (Data Science) (역사 | 편집) [2,246 바이트] 핵톤 (토론 | 기여) (Created page with "'''Specificity''', also known as the '''True Negative Rate (TNR)''', is a metric used in binary classification to measure the proportion of actual negative cases that are correctly identified by the model. It reflects the model’s ability to avoid false positives and accurately classify negative instances. ==Definition== Specificity is calculated as: :'''<big>Specificity = True Negatives / (True Negatives + False Positives)</big>''' A higher specificity value indicates...") 태그: 시각 편집
- 2024년 11월 4일 (월) 13:57 False Positive Rate (역사 | 편집) [2,077 바이트] 핵톤 (토론 | 기여) (Created page with "The '''False Positive Rate (FPR)''' is a metric used in binary classification to measure the proportion of actual negatives that are incorrectly identified as positives by the model. It is an important metric for understanding the model's tendency to produce false alarms. ==Definition== The False Positive Rate is calculated as: :'''FPR = False Positives / (False Positives + True Negatives)''' This metric represents the likelihood of a negative instance being misclassifie...") 태그: 시각 편집
- 2024년 11월 4일 (월) 12:15 Area Under the Curve (역사 | 편집) [2,354 바이트] 핵톤 (토론 | 기여) (Created page with "The Area Under the Curve (AUC) is a metric used in classification tasks to evaluate the overall performance of a binary classification model. It represents the area under the ROC (Receiver Operating Characteristic) Curve, providing a single value that summarizes the model’s ability to distinguish between positive and negative classes across all thresholds. ==Definition== AUC values range from 0 to 1: *'''AUC = 1''': Indicates a perfect classifier that co...") 태그: 시각 편집
- 2024년 11월 4일 (월) 12:13 ROC Curve (역사 | 편집) [2,501 바이트] 핵톤 (토론 | 기여) (Created page with "The '''ROC (Receiver Operating Characteristic) Curve''' is a graphical representation used to evaluate the performance of a binary classification model. It plots the true positive rate (sensitivity) against the false positive rate (1 - specificity) at various threshold settings, providing insight into the trade-offs between sensitivity and specificity. ==Definition== The ROC Curve is created by plotting: *'''True Positive Rate (TPR)''' or Sensitivity: TPR = True Positive...") 태그: 시각 편집
- 2024년 11월 4일 (월) 12:05 Classification Metrics (역사 | 편집) [3,024 바이트] 핵톤 (토론 | 기여) (Created page with "'''Classification metrics''' are evaluation measures used to assess the performance of classification models in machine learning and data science. These metrics help determine how well a model can predict the correct class labels, particularly in supervised learning tasks. ==Common Classification Metrics== There are several widely used classification metrics, each serving different aspects of model performance: *'''Accuracy''': Measures the ratio of correct predictions t...") 태그: 시각 편집
- 2024년 11월 4일 (월) 12:03 Confusion Matrix (역사 | 편집) [2,343 바이트] 핵톤 (토론 | 기여) (Created page with "'''Confusion Matrix''' is a tool used in data science and machine learning to evaluate the performance of a classification model. It provides a tabular summary of the model's predictions against the actual values, breaking down the number of correct and incorrect predictions for each class. ==Structure== The confusion matrix is typically a 2x2 table for binary classification, with the following layout: *'''True Positives (TP)''': Correctly predicted positive instances *...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:59 Recall (Data Science) (역사 | 편집) [2,267 바이트] 핵톤 (토론 | 기여) (Created page with "'''Recall''' is a metric used in data science, particularly in classification problems, to measure the completeness of positive predictions. It represents the ratio of true positive predictions to the sum of true positives and false negatives, reflecting the model's ability to identify all relevant instances within the data. ==Definition== Recall is calculated as: :'''Recall = True Positives / (True Positives + False Negatives)''' This metric is crucial when the focus is...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:58 Precision (Data Science) (역사 | 편집) [2,396 바이트] 핵톤 (토론 | 기여) (Created page with "'''Precision''' is a metric used in data science, particularly in classification problems, to measure the accuracy of positive predictions. It is defined as the ratio of true positive predictions to the sum of true positive and false positive predictions, offering insights into the model's performance in correctly identifying positive instances. ==Definition== Precision is calculated as: :'''<big>Precision = True Positives / (True Positives + False Positives)</big>''' Th...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:55 Accuracy (Data Science) (역사 | 편집) [2,174 바이트] 핵톤 (토론 | 기여) (Created page with "Accuracy is a metric used in data science to measure the performance of a model, particularly in classification problems. It represents the ratio of correctly predicted instances to the total number of instances. ==Definition== Accuracy is calculated as: :'''<big>Accuracy = (True Positives + True Negatives) / (Total Number of Instances)</big>''' This metric is often used in classification problems, where the goal is to determine how well a model can predict class labels....") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:51 Gini Impurity (Data Science) (역사 | 편집) [2,355 바이트] 핵톤 (토론 | 기여) (Created page with "'''Gini Impurity''' is a metric used in data science, particularly in decision tree algorithms, to measure the "impurity" or diversity of a dataset. It helps in determining how well a split at a node separates the data into distinct classes, making it essential for classification problems. ==Definition== Gini impurity calculates the probability that a randomly chosen element from a dataset will be incorrectly classified if it is randomly labeled accordi...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:46 Entropy (Data Science) (역사 | 편집) [5,845 바이트] 핵톤 (토론 | 기여) (Created page with "'''Entropy (Data Science)''' In '''Data Science''', '''Entropy''' is a measure of randomness or uncertainty in a dataset. Often used in Decision Trees and other machine learning algorithms, entropy quantifies the impurity or unpredictability of information in a set of data. In classification tasks, entropy helps determine the best way to split data to reduce uncertainty and increase homogeneity in the resulting subsets. ==How Entropy Works== Entropy, denoted as H, is ca...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:43 Decision Tree (역사 | 편집) [3,463 바이트] 핵톤 (토론 | 기여) (Created page with "'''Decision Tree''' A '''Decision Tree''' is a supervised learning algorithm used for both classification and regression tasks. It structures decisions as a tree-like model, where each internal node represents a test on a feature, each branch represents an outcome of that test, and each leaf node represents a class label or prediction. Decision Trees are highly interpretable and can work with both categorical and numerical data, making them widely applicable across vari...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:39 Random Forest (역사 | 편집) [3,898 바이트] 핵톤 (토론 | 기여) (Created page with "'''Random Forest''' is an ensemble learning method that combines multiple Decision Trees to improve classification or regression accuracy. It is designed to mitigate the limitations of single Decision Trees, such as overfitting and sensitivity to data variations, by building a "forest" of trees and aggregating their predictions. This approach often leads to greater model stability and accuracy. ==How It Works== Random Forest creates multiple Decision Trees during trainin...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:33 Logistic Regression (역사 | 편집) [3,911 바이트] 핵톤 (토론 | 기여) (Created page with "'''Logistic regression''' is a statistical and machine learning algorithm used for binary classification tasks, where the output variable is categorical and typically represents two classes (e.g., yes/no, spam/not spam, fraud/not fraud). Despite its name, Logistic Regression is a classification algorithm, not a regression algorithm, as it predicts probabilities of classes rather than continuous values. ==How It Works== Logistic Regression models the probability of a bin...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:29 Support Vector Machine (역사 | 편집) [4,232 바이트] 핵톤 (토론 | 기여) (Created page with "'''Support Vector Machine (SVM)''' is a powerful supervised machine learning algorithm used for both classification and regression tasks, though it is primarily used in classification. SVM works by finding the optimal boundary, or hyperplane, that best separates the data points of different classes. SVM is effective in high-dimensional spaces and is especially suitable for binary classification problems. ==How It Works== SVM aims to maximize the margin between data point...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:18 Independence (Linear Regression) (역사 | 편집) [2,421 바이트] 핵톤 (토론 | 기여) (Created page with "In the context of '''Linear Regression''', '''independence''' refers to the assumption that each observation in the dataset is independent of the others. This assumption is crucial for producing unbiased estimates and valid predictions. When observations are independent, it implies that the value of one observation does not influence or provide information about another observation. ==Importance of the Independence Assumption== Independence is a foundational assumption f...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:15 Linear Regression (역사 | 편집) [3,427 바이트] 핵톤 (토론 | 기여) (Created page with "'''Linear Regression''' is a fundamental regression algorithm used in machine learning and statistics to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the variables, which means the change in the dependent variable is proportional to the change in the independent variables. Linear Regression is commonly used for predictive analysis and trend forecasting. ==Types of Linear Regression== T...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:08 Discrete (역사 | 편집) [2,011 바이트] 핵톤 (토론 | 기여) (Created page with "In mathematics and computer science, '''discrete''' refers to distinct, separate values or entities, as opposed to continuous values. Discrete data or structures consist of isolated points or categories, often represented by integers or categorical labels. In contrast, continuous data have values that fall within a range and can take on any value within that interval. ==Examples of Discrete Data== Discrete data is commonly found in many fields and applications: *'''Count...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:05 Classification Algorithm (역사 | 편집) [4,578 바이트] 핵톤 (토론 | 기여) (Created page with "'''Classification algorithms''' are a group of machine learning methods used to categorize data into discrete classes or labels. These algorithms learn from labeled data during training and make predictions by assigning an input to one of several possible categories. Classification is widely applied in areas like image recognition, spam filtering, and medical diagnosis. ==Types of Classification Algorithms== There are various types of classification algorithms, each with...") 태그: 시각 편집
- 2024년 11월 4일 (월) 11:00 Regression Algorithm (역사 | 편집) [4,309 바이트] 핵톤 (토론 | 기여) (Created page with "'''Regression algorithms''' are a family of machine learning methods used for predicting continuous numerical values based on input features. Unlike classification, which predicts discrete classes, regression predicts outputs that can take any real number value. Regression algorithms are widely used in various fields, such as finance, economics, and environmental science, where predicting quantities (like stock prices, sales, or temperatures) is essential. ==Types of Reg...") 태그: 시각 편집
- 2024년 11월 4일 (월) 10:57 K-Nearest Neighbor (역사 | 편집) [3,441 바이트] 핵톤 (토론 | 기여) (Created page with "'''K-Nearest Neighbo'''r, often abbreviated as '''K-NN''', is a simple and intuitive classification and regression algorithm used in supervised machine learning. It classifies new data points based on the majority class among its nearest neighbors in the feature space. K-NN is a non-parametric algorithm, meaning it makes no assumptions about the underlying data distribution, making it v...") 태그: 시각 편집
