Logistic Regression : classification algorithm
0 likes173 views
Logistic regression is a classification algorithm used to predict binary outcomes based on one or more predictor variables. It employs the logit function to estimate the probability of an event, with wide applications in fields like medicine and marketing. While it is easy to implement and interpret, it struggles with a high number of categorical features and highly correlated independent variables.
1 of 16
Downloaded 13 times
Ad
Recommended
lec+5+_part+1 cloud .pptx
lec+5+_part+1 cloud .pptxsamaghorab Logistic regression is a supervised statistical technique used primarily for binary classification problems, estimating the probability of a dependent variable and utilizing logit and sigmoid functions to derive relationships between variables. It distinguishes classes by finding decision boundaries and is often preferred for its simplicity and interpretability, although it is limited in handling categorical features and may suffer from overfitting. The cost function in logistic regression is crucial for model performance and optimization during predictions.
Logistic Regression.pptx
Logistic Regression.pptxMuskaan194530 Logistic regression is a machine learning classification algorithm used to predict binary outcomes. It works by taking input variables and transforming them into a probability value between 0 and 1 using the logistic function. This allows logistic regression to be used for classification problems where the output is discrete, such as predicting if an event will occur or not. Some key assumptions of logistic regression include the dependent variable being binary, a linear relationship between the log odds of the dependent variable and independent variables, independence of errors, and no multicollinearity among independent variables. Logistic regression is commonly used for applications like fraud detection, disease diagnosis, and spam filtering.
Logistic Regression power point presentation.pptx
Logistic Regression power point presentation.pptxharshasawa2003 Logistic regression is a supervised machine learning algorithm used for binary and multi-class classification tasks, predicting the probability of an instance belonging to a specific class based on independent variables. It differs from linear regression, which predicts continuous numeric values, by outputting a probability value between 0 and 1 through a sigmoid function. While logistic regression is efficient and effective for binary classification, it has limitations such as sensitivity to outliers, challenges with high-dimensional data, and difficulties in handling imbalanced datasets.
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...Simplilearn The document serves as an introduction to machine learning basics, particularly focusing on supervised learning and the classification algorithm logistic regression. It explains the differences between linear and logistic regression, their applications, and includes practical examples like predicting the survival of Titanic passengers and identifying numbers in images. Additionally, it covers concepts like decision trees, k-nearest neighbors, and provides a mathematical understanding of logistic regression through its equations.
Logistic Regression.pptx
Logistic Regression.pptxVishnuKrishnan45 This document provides an introduction to logistic regression, including an overview of its key concepts and applications. Logistic regression can be used for classification problems where the dependent variable is binary. It uses a logistic function to model the probabilities of different outcomes. The loss function for logistic regression is log loss, as it aims to model binary classification probabilities between 0 and 1. Real-world examples of logistic regression include predicting customer purchases, detecting heart disease, and identifying fraudulent activities.
logistic regression in Data science Presentation
logistic regression in Data science PresentationARUN R S Logistic regression is a popular supervised learning algorithm used to predict categorical outcomes, converting linear regression outputs into probabilistic values via the sigmoid function. It comes in three types: binomial, multinomial, and ordinal, and has specific assumptions such as linearity and no multicollinearity. Applications span various fields including marketing, healthcare, finance, and social sciences, but limitations include interpretability issues and sensitivity to data quality.
Logistics Regression Using Python.pptx
Logistics Regression Using Python.pptxSharmilaMore5 Dr. Sharmila Sharad More discusses regression analysis using Python. Regression analysis seeks relationships between variables, such as how employee salaries relate to experience, education, role, and city. Logistic regression specifically is used for binary classification problems to predict whether an event occurs or not based on independent variables. Examples include predicting email spam, fraudulent transactions, and whether tumors are malignant or benign. Logistic regression calculates the probability of a binary event and deals with classification issues.
logisticregression-190726150723.pdf
logisticregression-190726150723.pdfSuaibDanish Logistic regression is a classification machine learning algorithm that predicts categorical dependent variables. It works by training a model on labeled data to understand the relationship between independent variables and the target variable. The model uses a logistic function to output probabilities between 0 and 1, which can then be used to classify observations into categories. Some common applications of logistic regression include predicting if a user will pay for a streaming service, the likelihood of a sports team winning, or classifying text as positive or negative. It is a simple yet effective algorithm that is widely used as a baseline for performance comparison.
Logistic regression : Use Case | Background | Advantages | Disadvantages
Logistic regression : Use Case | Background | Advantages | DisadvantagesRajat Sharma Logistic regression is a supervised learning classification model that analyzes datasets with independent variables to predict binary or multinomial outcomes using maximum likelihood. It is preferred when there is no linear relationship between variables and is robust to outliers, but relies heavily on proper data representation. Key components include hyperparameter optimization, a sigmoid function for predictions, and it is efficient and interpretable, albeit limited in predicting categorical outcomes.
Group 20_Logistic Regression devara.pptx
Group 20_Logistic Regression devara.pptxsriaditya070304 Binary logistic regression (𝑦=0 or 𝑦=1) can be calculated using IRLS which is equivalent to maximizing the log-likelihood of a Bernoulli distributed process using Newton’s method. If the problem is written in vector matrix form, with
3ml.pdf
3ml.pdfMianAdnan27 This document discusses machine learning algorithms for classification problems, specifically logistic regression. It explains that logistic regression predicts the probability of a binary outcome using a sigmoid function. Unlike linear regression, which is used for continuous outputs, logistic regression is used for classification problems where the output is discrete/categorical. It describes how logistic regression learns model parameters through gradient descent optimization of a likelihood function to minimize error. Regularization techniques can also be used to address overfitting issues that may arise from limited training data.
Introduction to Logistic Regression and Its Applications.pptx
Introduction to Logistic Regression and Its Applications.pptxkcharizmacruz Logistic regression predicts the probability of a binary outcome and is widely used in healthcare, marketing, finance and other fields. It differs from linear regression by predicting probabilities rather than continuous values. Examples of logistic regression applications include predicting the likelihood of a patient having an illness based on medical history, the probability of a customer purchasing a product based on demographics, and the chance of a loan defaulting using factors like credit scores.
Logistic regression in Machine Learning
Logistic regression in Machine LearningKuppusamy P Logistic regression is a predictive analysis algorithm that can be used for classification problems. It estimates the probabilities of different classes using the logistic function, which outputs values between 0 and 1. Logistic regression transforms its output using the sigmoid function to return a probability value. It is used for problems like email spam detection, fraud detection, and tumor classification. The independent variables should be independent of each other and the dependent variable must be categorical. Gradient descent is used to minimize the loss function and optimize the model parameters during training.
Logistic regression
Logistic regressionRupak Roy Logistic regression is a regression analysis technique used for predicting a binary outcome based on independent variables. It models the log odds of the dependent variable rather than the variable itself and includes various types like binary, ordered, and multinomial logit. The method involves interpreting the output through concepts such as odds ratio, confusion matrix, and model validation techniques like ROC curves and kappa statistics.
Logistic regression.ppt
Logistic regression.pptBalaChowdappa1 Logistic regression is a classification algorithm used to predict binary outcomes represented as categorical values like yes/no, true/false, etc. It fits an 'S' shaped logistic function to predict two maximum values between 0 and 1, representing probabilities. The sigmoid function maps predicted values to probabilities between 0 and 1. There is a threshold value that defines the probability of being 0 or 1, with values above the threshold tending to 1 and below tending to 0. The logistic regression equation is derived from the linear regression equation by taking the logarithm of odds ratios to fit within the 0 to 1 range.
Machine Learning-Lec5.pdf_explain of logistic regression
Machine Learning-Lec5.pdf_explain of logistic regressionBeshoyArnest The document provides an introduction to logistic regression, detailing its application as a statistical technique for binary classification problems where the target variable is categorical. It explains the logistic function's role in estimating probabilities and outlines the process of building a predictive model using training data. Additionally, it highlights the importance of the odds ratio and evaluation metrics in assessing classification models.
Unit2_Logistic regression_machine learning.pdf
Unit2_Logistic regression_machine learning.pdfRAMESHWAR CHINTAMANI Linear Regression, Logistic Regression, Ridge Regression, Lasso Regression,
Polynomial Regression Types of Regression, performance metrics Mean Squared Error
(MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R2 (R-
Squared)
Logistic Regression in machine learning.docx
Logistic Regression in machine learning.docxAbhaBansal8 Logistic regression is a classification algorithm used to predict categorical dependent variables. It estimates the probability of an instance belonging to a category based on independent variable values. Logistic regression models the log odds of the dependent variable as a linear combination of the independent variables. The output is a probability value between 0 and 1 that can be mapped to binary categories using a threshold. Examples of logistic regression applications include predicting heart attacks, university enrollment, and identifying spam emails.
Logistic regression
Logistic regressionDrZahid Khan This document provides an overview of logistic regression, including when and why it is used, the theory behind it, and how to assess logistic regression models. Logistic regression predicts the probability of categorical outcomes given categorical or continuous predictor variables. It relaxes the normality and linearity assumptions of linear regression. The relationship between predictors and outcomes is modeled using an S-shaped logistic function. Model fit, predictors, and interpretations of coefficients are discussed.
Linear Regression and Logistic Regression in ML
Linear Regression and Logistic Regression in MLKumud Arora Linear regression and logistic regression are statistical modeling techniques. Linear regression predicts continuous dependent variables using independent variables, while logistic regression predicts binary dependent variables. Both aim to model relationships between variables by estimating coefficients. Logistic regression models the log odds of the dependent variable rather than the variable directly. Key evaluation metrics for regression include accuracy, precision, recall, and F1 score, which are calculated using a confusion matrix.
Logistic regression
Logistic regressionYashwantGahlot1 Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
Machine Learning.pdf
Machine Learning.pdfUniversity of Sindh This document provides an overview of machine learning and linear regression. It defines machine learning as a segment of artificial intelligence that allows computers to learn from data without being explicitly programmed. The document then discusses linear regression as an algorithm that finds a linear relationship between variables to predict future outcomes. It provides the linear regression equation and describes simple, multiple, and non-linear regression. Examples of using linear regression in various industries are also given along with best practices.
Logistic Regression Classifier - Conceptual Guide
Logistic Regression Classifier - Conceptual GuideCaglar Subasi This document serves as a conceptual manual on logistic regression, a supervised machine learning technique used for classification tasks, particularly in the financial sector for detecting defaulters. It outlines the workflow of logistic regression, including data structure, experiment design, decision functions, and optimization processes. The article aims to clarify the theoretical aspects of logistic regression that are often overlooked in empirical studies.
7. logistics regression using spss
7. logistics regression using spssDr Nisha Arora This document provides guidance on performing and interpreting logistic regression analyses in SPSS. It discusses selecting appropriate statistical tests based on variable types and study objectives. It covers assumptions of logistic regression like linear relationships between predictors and the logit of the outcome. It also explains maximum likelihood estimation, interpreting coefficients, and evaluating model fit and accuracy. Guidelines are provided on reporting logistic regression results from SPSS outputs.
How to conduct a scooping Review .pptx.pdf
How to conduct a scooping Review .pptx.pdfhabibaabderrahim1 The document outlines a scoping review protocol focusing on specific phobia, particularly fear of flying. It defines specific phobia, discusses its prevalence, and details the process for conducting a scoping review, including inclusion and exclusion criteria, research questions about treatment technologies, and the role of virtual reality in treatment. The goal is to identify effective software for treating flight phobia and gather relevant data from various sources.
AthleteMentalHealthMental Fatigue Management.pptx.pdf
AthleteMentalHealthMental Fatigue Management.pptx.pdfhabibaabderrahim1 The document discusses the importance of monitoring mental health in athletes, particularly in professional football, with a focus on how mental health affects performance and well-being. It outlines common mental health issues, such as anxiety and depression, and emphasizes the need for proactive detection and monitoring using various tools. Recommendations for psychological skills development and tracking cognitive performance indicators are also provided.
More Related Content
Similar to Logistic Regression : classification algorithm (20)
logisticregression-190726150723.pdf
logisticregression-190726150723.pdfSuaibDanish Logistic regression is a classification machine learning algorithm that predicts categorical dependent variables. It works by training a model on labeled data to understand the relationship between independent variables and the target variable. The model uses a logistic function to output probabilities between 0 and 1, which can then be used to classify observations into categories. Some common applications of logistic regression include predicting if a user will pay for a streaming service, the likelihood of a sports team winning, or classifying text as positive or negative. It is a simple yet effective algorithm that is widely used as a baseline for performance comparison.
Logistic regression : Use Case | Background | Advantages | Disadvantages
Logistic regression : Use Case | Background | Advantages | DisadvantagesRajat Sharma Logistic regression is a supervised learning classification model that analyzes datasets with independent variables to predict binary or multinomial outcomes using maximum likelihood. It is preferred when there is no linear relationship between variables and is robust to outliers, but relies heavily on proper data representation. Key components include hyperparameter optimization, a sigmoid function for predictions, and it is efficient and interpretable, albeit limited in predicting categorical outcomes.
Group 20_Logistic Regression devara.pptx
Group 20_Logistic Regression devara.pptxsriaditya070304 Binary logistic regression (𝑦=0 or 𝑦=1) can be calculated using IRLS which is equivalent to maximizing the log-likelihood of a Bernoulli distributed process using Newton’s method. If the problem is written in vector matrix form, with
3ml.pdf
3ml.pdfMianAdnan27 This document discusses machine learning algorithms for classification problems, specifically logistic regression. It explains that logistic regression predicts the probability of a binary outcome using a sigmoid function. Unlike linear regression, which is used for continuous outputs, logistic regression is used for classification problems where the output is discrete/categorical. It describes how logistic regression learns model parameters through gradient descent optimization of a likelihood function to minimize error. Regularization techniques can also be used to address overfitting issues that may arise from limited training data.
Introduction to Logistic Regression and Its Applications.pptx
Introduction to Logistic Regression and Its Applications.pptxkcharizmacruz Logistic regression predicts the probability of a binary outcome and is widely used in healthcare, marketing, finance and other fields. It differs from linear regression by predicting probabilities rather than continuous values. Examples of logistic regression applications include predicting the likelihood of a patient having an illness based on medical history, the probability of a customer purchasing a product based on demographics, and the chance of a loan defaulting using factors like credit scores.
Logistic regression in Machine Learning
Logistic regression in Machine LearningKuppusamy P Logistic regression is a predictive analysis algorithm that can be used for classification problems. It estimates the probabilities of different classes using the logistic function, which outputs values between 0 and 1. Logistic regression transforms its output using the sigmoid function to return a probability value. It is used for problems like email spam detection, fraud detection, and tumor classification. The independent variables should be independent of each other and the dependent variable must be categorical. Gradient descent is used to minimize the loss function and optimize the model parameters during training.
Logistic regression
Logistic regressionRupak Roy Logistic regression is a regression analysis technique used for predicting a binary outcome based on independent variables. It models the log odds of the dependent variable rather than the variable itself and includes various types like binary, ordered, and multinomial logit. The method involves interpreting the output through concepts such as odds ratio, confusion matrix, and model validation techniques like ROC curves and kappa statistics.
Logistic regression.ppt
Logistic regression.pptBalaChowdappa1 Logistic regression is a classification algorithm used to predict binary outcomes represented as categorical values like yes/no, true/false, etc. It fits an 'S' shaped logistic function to predict two maximum values between 0 and 1, representing probabilities. The sigmoid function maps predicted values to probabilities between 0 and 1. There is a threshold value that defines the probability of being 0 or 1, with values above the threshold tending to 1 and below tending to 0. The logistic regression equation is derived from the linear regression equation by taking the logarithm of odds ratios to fit within the 0 to 1 range.
Machine Learning-Lec5.pdf_explain of logistic regression
Machine Learning-Lec5.pdf_explain of logistic regressionBeshoyArnest The document provides an introduction to logistic regression, detailing its application as a statistical technique for binary classification problems where the target variable is categorical. It explains the logistic function's role in estimating probabilities and outlines the process of building a predictive model using training data. Additionally, it highlights the importance of the odds ratio and evaluation metrics in assessing classification models.
Unit2_Logistic regression_machine learning.pdf
Unit2_Logistic regression_machine learning.pdfRAMESHWAR CHINTAMANI Linear Regression, Logistic Regression, Ridge Regression, Lasso Regression,
Polynomial Regression Types of Regression, performance metrics Mean Squared Error
(MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R2 (R-
Squared)
Logistic Regression in machine learning.docx
Logistic Regression in machine learning.docxAbhaBansal8 Logistic regression is a classification algorithm used to predict categorical dependent variables. It estimates the probability of an instance belonging to a category based on independent variable values. Logistic regression models the log odds of the dependent variable as a linear combination of the independent variables. The output is a probability value between 0 and 1 that can be mapped to binary categories using a threshold. Examples of logistic regression applications include predicting heart attacks, university enrollment, and identifying spam emails.
Logistic regression
Logistic regressionDrZahid Khan This document provides an overview of logistic regression, including when and why it is used, the theory behind it, and how to assess logistic regression models. Logistic regression predicts the probability of categorical outcomes given categorical or continuous predictor variables. It relaxes the normality and linearity assumptions of linear regression. The relationship between predictors and outcomes is modeled using an S-shaped logistic function. Model fit, predictors, and interpretations of coefficients are discussed.
Linear Regression and Logistic Regression in ML
Linear Regression and Logistic Regression in MLKumud Arora Linear regression and logistic regression are statistical modeling techniques. Linear regression predicts continuous dependent variables using independent variables, while logistic regression predicts binary dependent variables. Both aim to model relationships between variables by estimating coefficients. Logistic regression models the log odds of the dependent variable rather than the variable directly. Key evaluation metrics for regression include accuracy, precision, recall, and F1 score, which are calculated using a confusion matrix.
Logistic regression
Logistic regressionYashwantGahlot1 Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
Machine Learning.pdf
Machine Learning.pdfUniversity of Sindh This document provides an overview of machine learning and linear regression. It defines machine learning as a segment of artificial intelligence that allows computers to learn from data without being explicitly programmed. The document then discusses linear regression as an algorithm that finds a linear relationship between variables to predict future outcomes. It provides the linear regression equation and describes simple, multiple, and non-linear regression. Examples of using linear regression in various industries are also given along with best practices.
Logistic Regression Classifier - Conceptual Guide
Logistic Regression Classifier - Conceptual GuideCaglar Subasi This document serves as a conceptual manual on logistic regression, a supervised machine learning technique used for classification tasks, particularly in the financial sector for detecting defaulters. It outlines the workflow of logistic regression, including data structure, experiment design, decision functions, and optimization processes. The article aims to clarify the theoretical aspects of logistic regression that are often overlooked in empirical studies.
7. logistics regression using spss
7. logistics regression using spssDr Nisha Arora This document provides guidance on performing and interpreting logistic regression analyses in SPSS. It discusses selecting appropriate statistical tests based on variable types and study objectives. It covers assumptions of logistic regression like linear relationships between predictors and the logit of the outcome. It also explains maximum likelihood estimation, interpreting coefficients, and evaluating model fit and accuracy. Guidelines are provided on reporting logistic regression results from SPSS outputs.
More from habibaabderrahim1 (13)
How to conduct a scooping Review .pptx.pdf
How to conduct a scooping Review .pptx.pdfhabibaabderrahim1 The document outlines a scoping review protocol focusing on specific phobia, particularly fear of flying. It defines specific phobia, discusses its prevalence, and details the process for conducting a scoping review, including inclusion and exclusion criteria, research questions about treatment technologies, and the role of virtual reality in treatment. The goal is to identify effective software for treating flight phobia and gather relevant data from various sources.
AthleteMentalHealthMental Fatigue Management.pptx.pdf
AthleteMentalHealthMental Fatigue Management.pptx.pdfhabibaabderrahim1 The document discusses the importance of monitoring mental health in athletes, particularly in professional football, with a focus on how mental health affects performance and well-being. It outlines common mental health issues, such as anxiety and depression, and emphasizes the need for proactive detection and monitoring using various tools. Recommendations for psychological skills development and tracking cognitive performance indicators are also provided.
ridge regression calories prediction.pdf
ridge regression calories prediction.pdfhabibaabderrahim1 The document discusses the prediction of calories burnt during exercise using machine learning techniques in Python, presenting a systematic approach through the Crisp-DM framework. It covers data understanding, preparation, and modeling, focusing on regression methods such as linear and ridge regression, along with the use of the XGBoost algorithm. The conclusion summarizes the effectiveness of these methods for accurate calorie prediction.
Fuzzy Logic Control of Blood Pressure during Anesthesia.pdf
Fuzzy Logic Control of Blood Pressure during Anesthesia.pdfhabibaabderrahim1 The document outlines the development of a fuzzy logic controller for regulating the depth of anesthesia and maintaining patient safety during surgical operations by automating control over anesthetic drug delivery. It emphasizes the importance of accurately managing patient parameters like blood pressure, which is traditionally used to evaluate anesthesia depth, while introducing fuzzy rules and linguistic variables to enhance control precision. Simulation results indicate that fuzzy control improves accuracy in maintaining blood pressure compared to manual methods.
Marketing Channel Reporting Too using PowerBI and Data Analysis
Marketing Channel Reporting Too using PowerBI and Data Analysishabibaabderrahim1 The document outlines a reporting tool for monitoring marketing channels to help clients optimize their marketing budgets. It details the stages of business understanding, data understanding, data preparation, and modeling, including analysis methods and feature selection for improved performance. Finally, it discusses the deployment of data visualization techniques using tools like Power BI for effective analysis.
Google File System: System and Design Overview
Google File System: System and Design Overviewhabibaabderrahim1 The document outlines the Google File System (GFS), a scalable distributed file system designed to manage large data-intensive applications using inexpensive commodity hardware. It details the architecture, including a master node and multiple chunk servers, and describes key features like fault tolerance, efficient data replication, and data integrity checks. The system is designed to handle hardware failures and maintain high availability while optimizing for large data chunks and high throughput.
Emotions ontology summarized presentaion
Emotions ontology summarized presentaionhabibaabderrahim1 This presentation summarise the work introduced in : @article{GIL2015610,
title = {Emotions ontology for collaborative modelling and learning of emotional responses},
journal = {Computers in Human Behavior},
volume = {51},
pages = {610-617},
year = {2015},
note = {Computing for Human Learning, Behaviour and Collaboration in the Social and Mobile Networks Era},
issn = {0747-5632},
doi = {https://doi.org/10.1016/j.chb.2014.11.100},
url = {https://www.sciencedirect.com/science/article/pii/S0747563215001417},
author = {Rosa Gil and Jordi Virgili-Gomá and Roberto García and Cindy Mason},
keywords = {Emotion, Ontology, Collaborative learning, Social networks, Knowledge representation, Affective computing}
}
Neo4j.pptx
Neo4j.pptxhabibaabderrahim1 This document outlines the use of graph databases and Neo4j to model student data. It discusses how graph databases store connected data through nodes and relationships. It then demonstrates how to import student assessment data into Neo4j and create nodes and relationships to link assessments to students, courses to students based on their enrollments, and classify students' skills levels. The document provides an example of different queries that can be run on the graph database to analyze the student data.
Analyzing Digital Banking.pptx
Analyzing Digital Banking.pptxhabibaabderrahim1 This paper discusses the use of text mining to analyze user reviews of digital banking applications to improve customer satisfaction and retention. It employs latent Dirichlet allocation (LDA) for topic modeling and association mining techniques to identify significant features and user sentiments from unstructured review data. The study aims to reveal which areas of the banking application need optimization by clustering user feedback into relevant topics and analyzing the relationships between features and user ratings.
map Reduce.pptx
map Reduce.pptxhabibaabderrahim1 This document provides an introduction to MapReduce programming model. It describes how MapReduce inspired by Lisp functions works by dividing tasks into mapping and reducing parts that are distributed and processed in parallel. It then gives examples of using MapReduce for word counting and calculating total sales. It also provides details on MapReduce daemons in Hadoop and includes demo code for summing array elements in Java and doing word counting on a text file using the Hadoop framework in Python.
SLR and SMR.pptx
SLR and SMR.pptxhabibaabderrahim1 This document provides an overview of literature reviews, systematic literature reviews, and systematic mapping reviews. It defines each type of review and explains their purposes. Literature reviews discuss published information on a topic but may be biased. Systematic literature reviews have an explicit methodology to minimize bias, including comprehensive search strategies and inclusion/exclusion criteria. Systematic mapping reviews aim to categorize and characterize existing research on a topic to identify gaps for future work. Both systematic reviews and mapping reviews provide transparent, reliable summaries of the available evidence.
Trapezoidal Velocity.pptx
Trapezoidal Velocity.pptxhabibaabderrahim1 The document presents a trapezoidal velocity profile generator designed for position control systems to improve fast and precise positioning. It discusses the problems with traditional profile generators and introduces a novel approach using a cascaded P-PI position controller to mitigate these issues. The solution aims to produce smoother trajectories for motion control by addressing errors in velocity and position profiles.
Social Recommendation a Review.pptx
Social Recommendation a Review.pptxhabibaabderrahim1 The document reviews social recommendation systems (SRS) and contrasts them with traditional recommendation systems, emphasizing their reliance on social connections and behaviors for making recommendations. It discusses various existing SRS methods, including memory-based and model-based approaches, and highlights evaluation metrics for their performance. Additionally, it explores challenges such as data sparsity and cold-start problems while suggesting future research directions to enhance SRS effectiveness.
Ad
Recently uploaded (20)
MEDIA_LITERACY_INDEX_OF_EDUCATORS_ENG.pdf
MEDIA_LITERACY_INDEX_OF_EDUCATORS_ENG.pdfOlhaTatokhina1 Report based on the findings of a quantitative research conducted by the research agency New Image Marketing Group, commissioned by the NGO Detector Media, compiled by PhD in Sociology Marta Naumova
apidays New York 2025 - Life is But a (Data) Stream by Sandon Jacobs (Confluent)
apidays New York 2025 - Life is But a (Data) Stream by Sandon Jacobs (Confluent)apidays Life is But a (Data) Stream: Building Quality Data Pipelines
Sandon Jacobs, Senior Developer Advocate at Confluent
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
FME Beyond Data Processing: Creating a Dartboard Accuracy App
FME Beyond Data Processing: Creating a Dartboard Accuracy Appjacoba18 Bij Nordend vroegen we ons af of FME ons kan helpen bepalen waar we moeten mikken voor de hoogste score, gebaseerd op onze nauwkeurigheid. We tonen hoe we met FME Flow, een zelfgemaakte WMS-dartbordlaag en geanalyseerde gooiresultaten optimale mikpunten berekenden.
Data Warehousing and Analytics IFI Techsolutions .pptx
Data Warehousing and Analytics IFI Techsolutions .pptxIFI Techsolutions IFI Techsolutions is a leading cloud solutions and managed services provider. We are an Azure Expert Managed Service Provider (AEMSP) and boast multiple advanced specializations in Infra and Database Migration to Microsoft Azure, Migrate Enterprise Apps to Microsoft Azure, and Microsoft Azure Virtual Desktop.
Simplify SQL migration to Azure with Microsoft’s DMA tool—assess, plan, and move your data seamlessly with expert support from IFI Tech.
最新版美国佐治亚大学毕业证（UGA毕业证书）原版定制
最新版美国佐治亚大学毕业证（UGA毕业证书）原版定制Taqyea 2025原版佐治亚大学毕业证书pdf电子版【q薇1954292140】美国毕业证办理UGA佐治亚大学毕业证书多少钱？【q薇1954292140】海外各大学Diploma版本，因为疫情学校推迟发放证书、证书原件丢失补办、没有正常毕业未能认证学历面临就业提供解决办法。当遭遇挂科、旷课导致无法修满学分，或者直接被学校退学，最后无法毕业拿不到毕业证。此时的你一定手足无措，因为留学一场，没有获得毕业证以及学历证明肯定是无法给自己和父母一个交代的。
【复刻佐治亚大学成绩单信封,Buy The University of Georgia Transcripts】
购买日韩成绩单、英国大学成绩单、美国大学成绩单、澳洲大学成绩单、加拿大大学成绩单（q微1954292140）新加坡大学成绩单、新西兰大学成绩单、爱尔兰成绩单、西班牙成绩单、德国成绩单。成绩单的意义主要体现在证明学习能力、评估学术背景、展示综合素质、提高录取率，以及是作为留信认证申请材料的一部分。
佐治亚大学成绩单能够体现您的的学习能力，包括佐治亚大学课程成绩、专业能力、研究能力。（q微1954292140）具体来说，成绩报告单通常包含学生的学习技能与习惯、各科成绩以及老师评语等部分，因此，成绩单不仅是学生学术能力的证明，也是评估学生是否适合某个教育项目的重要依据！
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【主营项目】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理毕业证|办理文凭: 买大学毕业证|买大学文凭【q薇1954292140】佐治亚大学学位证明书如何办理申请？
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理美国成绩单佐治亚大学毕业证【q薇1954292140】国外大学毕业证, 文凭办理, 国外文凭办理, 留信网认证
SQL-Demystified-A-Beginners-Guide-to-Database-Mastery.pptx
SQL-Demystified-A-Beginners-Guide-to-Database-Mastery.pptxbhavaniteacher99 Unlock the power of data with “SQL Demystified” — your perfect beginner-friendly guide to mastering Structured Query Language (SQL)! Whether you're a student, aspiring analyst, or a professional stepping into the world of data, this detailed and practical PowerPoint presentation simplifies the fundamentals and builds your SQL skills step-by-step.
📘 What’s Inside?
Introduction to SQL and its real-world importance
Understanding database structures: tables, columns & relationships
Core SQL queries: SELECT, FROM, WHERE, ORDER BY
Hands-on examples and practice exercises
Advanced SQL: JOIN, subqueries, and aggregate functions
Data manipulation: INSERT, UPDATE, DELETE
Performance tips and indexing basics
SQL security best practices and learning paths for future growth
🧠 Who is it for?
Beginners eager to learn SQL
Data science and computer students
Business professionals and analysts
Anyone curious about how databases work
✅ Features:
Clear visuals and examples
Easy-to-follow explanations
Real-life applications in banking, healthcare, e-commerce, and more
Reusable and editable presentation file (PPTX format)
Take your first step toward data mastery with confidence. Learn SQL the smart way — with structure, clarity, and purpose.
5. & 9. Packing material and Labelling_AP-60,XP-60.pdf
5. & 9. Packing material and Labelling_AP-60,XP-60.pdfmaricruzduranpaterni Especificaciones de material de empaque
SAP_S4HANA_EWM_Food_Processing_Industry.pptx
SAP_S4HANA_EWM_Food_Processing_Industry.pptxvemulavenu484 SAP Extended Warehouse Management (EWM) is a part of SAP S/4HANA offering advanced warehouse and logistics capabilities. It enables efficient handling of goods movement, storage, and inventory in real-time.
apidays Singapore 2025 - Building Finance Innovation Ecosystems by Umang Moon...
apidays Singapore 2025 - Building Finance Innovation Ecosystems by Umang Moon...apidays Building Finance Innovation Ecosystems
Umang Moondra, CEO at APIX
apidays Singapore 2025
Where APIs Meet AI: Building Tomorrow's Intelligent Ecosystems
April 15 & 16, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Veilig en vlot fietsen in Oost-Vlaanderen: Fietssnelwegen geoptimaliseerd met...
Veilig en vlot fietsen in Oost-Vlaanderen: Fietssnelwegen geoptimaliseerd met...jacoba18 Dit project, in samenwerking met de provincie Oost-Vlaanderen, heeft als doel de veiligheid op de F45 tussen Oudenaarde en Gent te verbeteren door netwerkdata aan te vullen met snelheidslimieten, toegangsbeperkingen en voorrangsregels. Met mobile mapping en FME werden verkeersborden en kruispunten geanalyseerd, waarna de resultaten in ArcGIS inzicht boden in toegankelijke wegsegmenten en verkeersregels.
Fundamental Analysis for Dummies.pdf somwmdw
Fundamental Analysis for Dummies.pdf somwmdwssuserc74044 axanxbajbjbjxxxxxxxxxxxx AHXBAHVBXHVVVVVVVVVVVVVV AASJAHSJAHSJA ASXHSKAXKAXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXAXANNN ZNXSBXJSBJX AIXHIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIA AXHIHHHHHHHHHHHHHHHHHHHHHHHHH AIXHIIIIIIIIIIIIIIIIIIIIIIIIIIII IAHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH AXHIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII SOMEM AXSKXJSKXJSKJASKJOSJCIUDHDUVBDJBN SICJIOSS HSSSSSSSSSSSSSSSSCBBBBBBBBBBBBBBBBSNCBSJCBSJCBSJCBSJCBSJBCJSBCJS XXXBHBXSBHJSB ONE DAY XNSBNSBXSJS SCSSJKSJ SOME TIWDSCNSCN S
11th International Conference on Data Mining (DaMi 2025)
11th International Conference on Data Mining (DaMi 2025)rinzindorjej Welcome To DAMI 2025
Submit Your Research Articles...!!!
11th International Conference on Data Mining (DaMi 2025)
July 26 ~ 27, 2025, London, United Kingdom
Submission Deadline : June 14, 2025
Paper Submission : https://csit2025.org/submission/index.php
Contact Us : Here's where you can reach us : [email protected] or [email protected]
For more details visit : Webpage : https://csit2025.org/dami/index
#datamining #datascience #dataanalytics #database #data #bigdata #datascientist #datavisualization #machinelearning #programming #dataanalysis #artificialintelligence #coding #analytics #dataanalyst #statistics #dataentry #deeplearning #python #rb #technology #datadriven #datacollection #ai #javascript #b #programmer #datacenter #webresearch #datamanagement
Residential Zone 4 for industrial village
Residential Zone 4 for industrial villageMdYasinArafat13 based on assumption that failure of such a weld is by shear on the
effective area whether the shear transfer is parallel to or
perpendicular to the axis of the line of fillet weld. In fact, the
strength is greater for shear transfer perpendicular to the weld axis;
however, for simplicity the situations are treated the same.
apidays New York 2025 - Using GraphQL SDL files as executable API Contracts b...
apidays New York 2025 - Using GraphQL SDL files as executable API Contracts b...apidays Using GraphQL SDL files as executable API Contracts
Hari Krishnan, Co-founder & CTO at Specmatic
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
apidays Singapore 2025 - 4 Identity Essentials for Scaling SaaS in Large Orgs...
apidays Singapore 2025 - 4 Identity Essentials for Scaling SaaS in Large Orgs...apidays 4 identity factors you didn't know you needed to support large organizations in your SaaS
Daizen Ikehara, Principal Developer Advocate at Auth0
apidays Singapore 2025
Where APIs Meet AI: Building Tomorrow's Intelligent Ecosystems
April 15 & 16, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
QUALITATIVE EXPLANATORY VARIABLES REGRESSION MODELS
QUALITATIVE EXPLANATORY VARIABLES REGRESSION MODELSAmeya Patekar QUALITATIVE EXPLANATORY VARIABLES REGRESSION MODELS
Ad
Logistic Regression : classification algorithm
- 2. Introduction logistic Regression : what , when and how ? Advantages and Disadvantages Uses cases Demo
- 3. Introduction What is Regression? Regression is one of the most common models of machine learning , it is a way of predicting future happenings between a dependent (target) and one or more independent (predictor) variables . 3 Regression Linear regression Logistic regression Polynomial regression
- 4. logistic Regression : What ? Logistic regression is a classification algorithm used to assign observations to a discrete set of classes , In other words,the outcome or target variable is dichotomous in nature , which means it can only have one of two values (either 0 or 1, true or false, black or white, spam or not spam, and so on … )
- 6. logistic Regression : How ? Logistic Regression predicts the probability of occurrence of a binary event utilizing what we call logit function or Sigmoid function . The hypothesis of logistic regression : ted will become 0.
- 7. The hypothesis of logistic regression : 1. Linear Regression fonction : 1. Sigmoid Function: 1. Apply Sigmoid function on linear regression:
- 8. Example Does a patient have a lung cancer ?
- 9. New Data
- 11. Optimization The Cost function of Logistic regression Cost Function The cost function represents optimization objective i.e. we create a cost function and minimize it so that we can develop an accurate model with minimum error.
- 12. Gradient Descent Gradient descent is an optimization algorithm , The main goal of it is to minimize the cost value. Objective: To minimize the cost function we have to run the gradient descent function on each parameter . 12
- 13. Uses Cases Logistic Regression has a wide range of real-life applications : medical fields 13 Social sciences Marketing fields
- 14. Advantages ❖ Useful on problems where you need to give more rationale for a prediction ❖ Doesn't require high computation power ❖ Easy to implement and to interpret ❖ Used widely by data analyst and scientist ❖ Logistic regression provides a probability score for observations
- 15. Disadvantages ❖ Logistic regression is not able to handle a large number of categorical features. ❖ Logistic regression will not perform well with independent variables that are not correlated to the target variable and are very similar or correlated to each other.
- 16. “ ▸ Thank you ! 16