핵톤의 사용자 기여
IT 위키
2024년 11월 5일 (화)
- 02:202024년 11월 5일 (화) 02:20 차이 역사 +26 First Principles Algorithm 편집 요약 없음 최신 태그: 시각 편집
- 02:202024년 11월 5일 (화) 02:20 차이 역사 +5,398 새글 First Principles Algorithm 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
- 02:172024년 11월 5일 (화) 02:17 차이 역사 +26 Learner (Data Science) 편집 요약 없음 최신 태그: 시각 편집
- 02:152024년 11월 5일 (화) 02:15 차이 역사 +3,994 새글 Learner (Data Science) 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
- 02:072024년 11월 5일 (화) 02:07 차이 역사 +26 Rows (Data Science) 편집 요약 없음 최신 태그: 시각 편집
- 02:072024년 11월 5일 (화) 02:07 차이 역사 +3,215 새글 Rows (Data Science) 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
- 02:042024년 11월 5일 (화) 02:04 차이 역사 +26 Feature (Data Science) 편집 요약 없음 최신 태그: 시각 편집
- 02:032024년 11월 5일 (화) 02:03 차이 역사 +3,295 새글 Feature (Data Science) 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)
- 14:442024년 11월 4일 (월) 14:44 차이 역사 +1,358 Data Science Cheat Sheet 편집 요약 없음 최신 태그: 시각 편집
- 14:432024년 11월 4일 (월) 14:43 차이 역사 +1,077 Data Science Cheat Sheet 편집 요약 없음 태그: 시각 편집
- 14:352024년 11월 4일 (월) 14:35 차이 역사 +3,384 새글 Cold Start Problem 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..." 최신 태그: 시각 편집
- 14:342024년 11월 4일 (월) 14:34 차이 역사 +820 Content-Based Filtering 편집 요약 없음 최신 태그: 시각 편집
- 14:342024년 11월 4일 (월) 14:34 차이 역사 +2,288 새글 Content-Based Filtering 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
- 14:332024년 11월 4일 (월) 14:33 차이 역사 +37 Recommender System 편집 요약 없음 최신 태그: 시각 편집
- 14:332024년 11월 4일 (월) 14:33 차이 역사 +23 Collaborative Filtering 편집 요약 없음 최신 태그: 시각 편집
- 14:332024년 11월 4일 (월) 14:33 차이 역사 +37 Collaborative Filtering 편집 요약 없음 태그: 시각 편집
- 14:322024년 11월 4일 (월) 14:32 차이 역사 +2,889 새글 분류:Artificial Intelligence Created page with "Artificial Intelligence (AI) is a branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence. This includes areas such as learning, reasoning, problem-solving, perception, and language understanding. AI is applied across a broad range of fields, including robotics, natural language processing, computer vision, and more. ==Subfields of Artificial Intelligence== AI encompasses various subfields, each address..." 최신 태그: 시각 편집
- 14:312024년 11월 4일 (월) 14:31 차이 역사 +3,738 새글 Collaborative Filtering 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
- 14:302024년 11월 4일 (월) 14:30 차이 역사 +26 Recommender System 편집 요약 없음 태그: 시각 편집
- 14:292024년 11월 4일 (월) 14:29 차이 역사 +3,803 새글 Recommender System 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
- 14:272024년 11월 4일 (월) 14:27 차이 역사 +34 새글 AUC Redirected page to Area Under the Curve 최신 태그: 새 넘겨주기 시각 편집
- 14:262024년 11월 4일 (월) 14:26 차이 역사 +3,840 새글 Precision-Recall Curve 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..." 최신 태그: 시각 편집
- 14:262024년 11월 4일 (월) 14:26 차이 역사 +1,871 Data Science Cheat Sheet 편집 요약 없음 태그: 시각 편집
- 14:202024년 11월 4일 (월) 14:20 차이 역사 +26 Cumulative Response Curve 편집 요약 없음 최신 태그: 시각 편집
- 14:192024년 11월 4일 (월) 14:19 차이 역사 +3,862 새글 Gain Chart 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..." 최신 태그: 시각 편집
- 14:192024년 11월 4일 (월) 14:19 차이 역사 +3,270 새글 Cumulative Response Curve 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
- 14:192024년 11월 4일 (월) 14:19 차이 역사 −3,232 Cumulative Response Curves Redirected page to Cumulative Response Curve 최신 태그: 새 넘겨주기 시각 편집
- 14:182024년 11월 4일 (월) 14:18 차이 역사 +3,328 새글 Lift Curve 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..." 최신 태그: 시각 편집
- 14:172024년 11월 4일 (월) 14:17 차이 역사 +3,297 새글 Cumulative Response Curves 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
- 14:162024년 11월 4일 (월) 14:16 차이 역사 +3,713 새글 Gain (Data Science) 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..." 최신 태그: 시각 편집
- 14:152024년 11월 4일 (월) 14:15 차이 역사 +3,303 새글 Lift (Data Science) 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..." 최신 태그: 시각 편집
- 14:122024년 11월 4일 (월) 14:12 차이 역사 +1,811 새글 Data Science Cheat Sheet 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
- 14:112024년 11월 4일 (월) 14:11 차이 역사 +2,246 새글 Specificity (Data Science) 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..." 최신 태그: 시각 편집
- 13:592024년 11월 4일 (월) 13:59 차이 역사 +35 새글 Sensitivity Redirected page to Recall (Data Science) 최신 태그: 새 넘겨주기 시각 편집
- 13:592024년 11월 4일 (월) 13:59 차이 역사 +35 새글 True Positive Rate Redirected page to Recall (Data Science) 최신 태그: 새 넘겨주기 시각 편집
- 13:572024년 11월 4일 (월) 13:57 차이 역사 +2,077 새글 False Positive Rate 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..." 최신 태그: 시각 편집
- 12:162024년 11월 4일 (월) 12:16 차이 역사 +38 Area Under the Curve 편집 요약 없음 최신 태그: 시각 편집
- 12:152024년 11월 4일 (월) 12:15 차이 역사 +2,316 새글 Area Under the Curve 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
- 12:132024년 11월 4일 (월) 12:13 차이 역사 +2,501 새글 ROC Curve 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..." 최신 태그: 시각 편집
- 12:082024년 11월 4일 (월) 12:08 차이 역사 +2,215 F1 Score 편집 요약 없음 최신 태그: 시각 편집
- 12:052024년 11월 4일 (월) 12:05 차이 역사 +3,024 새글 Classification Metrics 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..." 최신 태그: 시각 편집
- 12:032024년 11월 4일 (월) 12:03 차이 역사 +2,343 새글 Confusion Matrix 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 *..." 최신 태그: 시각 편집
- 12:012024년 11월 4일 (월) 12:01 차이 역사 +121 Main Page 편집 요약 없음 태그: 시각 편집: 전환됨
- 11:592024년 11월 4일 (월) 11:59 차이 역사 +26 Recall (Data Science) 편집 요약 없음 최신 태그: 시각 편집
- 11:592024년 11월 4일 (월) 11:59 차이 역사 +2,241 새글 Recall (Data Science) 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
- 11:582024년 11월 4일 (월) 11:58 차이 역사 +2,396 새글 Precision (Data Science) 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..." 최신 태그: 시각 편집
- 11:572024년 11월 4일 (월) 11:57 차이 역사 +73 Accuracy (Data Science) 편집 요약 없음 최신 태그: 시각 편집
- 11:552024년 11월 4일 (월) 11:55 차이 역사 +37 새글 Accuracy Redirected page to Accuracy (Data Science) 최신 태그: 새 넘겨주기 시각 편집
- 11:552024년 11월 4일 (월) 11:55 차이 역사 +2,101 새글 Accuracy (Data Science) 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
- 11:512024년 11월 4일 (월) 11:51 차이 역사 +2,355 새글 Gini Impurity (Data Science) 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..." 최신 태그: 시각 편집
