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- 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 Gini Impurity (역사 | 편집) [42 바이트] 핵톤 (토론 | 기여) (Redirected page to Gini Impurity (Data Science)) 태그: 새 넘겨주기 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) 05:58 Entropy (역사 | 편집) [36 바이트] 핵톤 (토론 | 기여) (Redirected page to Entropy (Data Science)) 태그: 새 넘겨주기 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) 05:52 Clustering (역사 | 편집) [34 바이트] 핵톤 (토론 | 기여) (Redirected page to Clustering Algorithm) 태그: 새 넘겨주기 시각 편집
- 2024년 11월 5일 (화) 05:52 Classification (역사 | 편집) [38 바이트] 핵톤 (토론 | 기여) (Redirected page to Classification Algorithm) 태그: 새 넘겨주기 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) 14:27 AUC (역사 | 편집) [34 바이트] 핵톤 (토론 | 기여) (Redirected page to Area Under the Curve) 태그: 새 넘겨주기 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) 14:17 Cumulative Response Curves (역사 | 편집) [65 바이트] 핵톤 (토론 | 기여) (Created page with "'''Cumulative Response Curves (CRC)''' are 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 percentag...") 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) 13:59 Sensitivity (역사 | 편집) [35 바이트] 핵톤 (토론 | 기여) (Redirected page to Recall (Data Science)) 태그: 새 넘겨주기 시각 편집
- 2024년 11월 4일 (월) 13:59 True Positive Rate (역사 | 편집) [35 바이트] 핵톤 (토론 | 기여) (Redirected page to Recall (Data Science)) 태그: 새 넘겨주기 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
