The document discusses clustering and nearest neighbor algorithms for deriving knowledge from data at scale. It provides an overview of clustering techniques like k-means clustering and discusses how they are used for applications such as recommendation systems. It also discusses challenges like class imbalance that can arise when applying these techniques to large, real-world datasets and evaluates different methods for addressing class imbalance. Additionally, it discusses performance metrics like precision, recall, and lift that can be used to evaluate models on large datasets.
The document discusses an agenda for a lecture on deriving knowledge from data at scale. The lecture will include a course project check-in, a thought exercise on data transformation, and a deeper dive into ensembling techniques. It also provides tips on gaining experience and intuition for data science, including becoming proficient in tools, deeply understanding algorithms, and focusing on specific data types through hands-on practice of experiments. Attribute selection techniques like filters, wrappers and embedded methods are also covered. Finally, the document discusses support vector machines and handling missing values in data.
The document discusses various machine learning concepts like model overfitting, underfitting, missing values, stratification, feature selection, and incremental model building. It also discusses techniques for dealing with overfitting and underfitting like adding regularization. Feature engineering techniques like feature selection and creation are important preprocessing steps. Evaluation metrics like precision, recall, F1 score and NDCG are discussed for classification and ranking problems. The document emphasizes the importance of feature engineering and proper model evaluation.
This document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
The document discusses a lecture on deriving knowledge from data at scale. It outlines topics that will be covered, including forecasting techniques, introducing the Weka data mining tool, decision trees, and doing hands-on exercises with decision trees in Weka. The lecture objectives are also listed, which are to gain familiarity with Weka, understand decision trees, and get experience applying decision trees in Weka if time permits.
This document appears to be lecture slides for a course on deriving knowledge from data at scale. It covers many topics related to building machine learning models including data preparation, feature selection, classification algorithms like decision trees and support vector machines, and model evaluation. It provides examples applying these techniques to a Titanic passenger dataset to predict survival. It emphasizes the importance of data wrangling and discusses various feature selection methods.
This document outlines an agenda for a data science boot camp covering various machine learning topics over several hours. The agenda includes discussions of decision trees, ensembles, random forests, data modelling, and clustering. It also provides examples of data leakage problems and discusses the importance of evaluating model performance. Homework assignments involve building models with Weka and identifying the minimum attributes needed to distinguish between red and white wines.
The document discusses various topics related to deriving knowledge from data at scale. It begins with definitions of a data scientist from different sources, noting that data scientists obtain, explore, model and interpret data using hacking, statistics and machine learning. It also discusses challenges of having enough data scientists. Other topics discussed include important ideas for data science like interdisciplinary work, algorithms, coding practices, data strategy, causation vs. correlation, and feedback loops. Building predictive models is also discussed with steps like defining objectives, accessing and understanding data, preprocessing, and evaluating models.
The document discusses feature extraction and selection as important steps in machine learning. It notes that better features often lead to better algorithms. It then describes five clusters identified through clustering analysis. Each cluster contains individuals (male or female) with certain average demographic characteristics like age, location, income, and whether they have accounts or loans. The document emphasizes that feature extraction and selection are underrated but important for machine learning.
The document discusses an agenda for a lecture on deriving knowledge from data at scale. The lecture will include a course project check-in, a thought exercise on data transformation, and a deeper dive into ensembling techniques. It also provides tips on gaining experience and intuition for data science, including becoming proficient in tools, deeply understanding algorithms, and focusing on specific data types through hands-on practice of experiments. Attribute selection techniques like filters, wrappers and embedded methods are also covered. Finally, the document discusses support vector machines and handling missing values in data.
The document discusses various machine learning concepts like model overfitting, underfitting, missing values, stratification, feature selection, and incremental model building. It also discusses techniques for dealing with overfitting and underfitting like adding regularization. Feature engineering techniques like feature selection and creation are important preprocessing steps. Evaluation metrics like precision, recall, F1 score and NDCG are discussed for classification and ranking problems. The document emphasizes the importance of feature engineering and proper model evaluation.
This document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
The document discusses a lecture on deriving knowledge from data at scale. It outlines topics that will be covered, including forecasting techniques, introducing the Weka data mining tool, decision trees, and doing hands-on exercises with decision trees in Weka. The lecture objectives are also listed, which are to gain familiarity with Weka, understand decision trees, and get experience applying decision trees in Weka if time permits.
This document appears to be lecture slides for a course on deriving knowledge from data at scale. It covers many topics related to building machine learning models including data preparation, feature selection, classification algorithms like decision trees and support vector machines, and model evaluation. It provides examples applying these techniques to a Titanic passenger dataset to predict survival. It emphasizes the importance of data wrangling and discusses various feature selection methods.
This document outlines an agenda for a data science boot camp covering various machine learning topics over several hours. The agenda includes discussions of decision trees, ensembles, random forests, data modelling, and clustering. It also provides examples of data leakage problems and discusses the importance of evaluating model performance. Homework assignments involve building models with Weka and identifying the minimum attributes needed to distinguish between red and white wines.
The document discusses various topics related to deriving knowledge from data at scale. It begins with definitions of a data scientist from different sources, noting that data scientists obtain, explore, model and interpret data using hacking, statistics and machine learning. It also discusses challenges of having enough data scientists. Other topics discussed include important ideas for data science like interdisciplinary work, algorithms, coding practices, data strategy, causation vs. correlation, and feedback loops. Building predictive models is also discussed with steps like defining objectives, accessing and understanding data, preprocessing, and evaluating models.
The document discusses feature extraction and selection as important steps in machine learning. It notes that better features often lead to better algorithms. It then describes five clusters identified through clustering analysis. Each cluster contains individuals (male or female) with certain average demographic characteristics like age, location, income, and whether they have accounts or loans. The document emphasizes that feature extraction and selection are underrated but important for machine learning.
Machine Learning has become a must to improve insight, quality and time to market. But it's also been called the 'high interest credit card of technical debt' with challenges in managing both how it's applied and how its results are consumed.
If you are curious what is ML all about, this is a gentle introduction to Machine Learning and Deep Learning. This includes questions such as why ML/Data Analytics/Deep Learning ? Intuitive Understanding o how they work and some models in detail. At last I share some useful resources to get started.
H2O World - Top 10 Data Science Pitfalls - Mark LandrySri Ambati
H2O World 2015 - Mark Landry
Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document provides an introduction to machine learning, including:
- It discusses how the human brain learns to classify images and how machine learning systems are programmed to perform similar tasks.
- It provides an example of image classification using machine learning and discusses how machines are trained on sample data and then used to classify new queries.
- It outlines some common applications of machine learning in areas like banking, biomedicine, and computer/internet applications. It also discusses popular machine learning algorithms like Bayes networks, artificial neural networks, PCA, SVM classification, and K-means clustering.
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
Fairly Measuring Fairness In Machine LearningHJ van Veen
This document discusses various approaches for measuring and achieving fairness in machine learning models. It summarizes research on identifying discrimination from models, removing protected features, and imposing different fairness constraints. Specifically, it finds that removing a protected feature like age can decrease model performance, redundant encodings may still encode that feature, and different fairness constraints like equalized odds come at a cost to model optimization but are important to consider.
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Hacking Predictive Modeling - RoadSec 2018HJ van Veen
This document provides an overview of machine learning and predictive modeling techniques for hackers and data scientists. It discusses foundational concepts in machine learning like functionalism, connectionism, and black box modeling. It also covers practical techniques like feature engineering, model selection, evaluation, optimization, and popular Python libraries. The document encourages an experimental approach to hacking predictive models through techniques like brute forcing hyperparameters, fuzzing with data permutations, and social engineering within data science communities.
Introduction to machine learning. Basics of machine learning. Overview of machine learning. Linear regression. logistic regression. cost function. Gradient descent. sensitivity, specificity. model selection.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Scienc...Edureka!
This Edureka Decision Tree tutorial will help you understand all the basics of Decision tree. This decision tree tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn decision tree analysis along with examples.
Below are the topics covered in this tutorial:
1) Machine Learning Introduction
2) Classification
3) Types of classifiers
4) Decision tree
5) How does Decision tree work?
6) Demo in R
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
H2O World - Top 10 Deep Learning Tips & Tricks - Arno CandelSri Ambati
H2O World 2015 - Arno Candel
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Data Science Training | Data Science For Beginners | Data Science With Python...Simplilearn
This Data Science presentation will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist.
This Data Science presentation will cover the following topics:
1. What is Data Science?
2. Who is a Data Scientist?
3. What does a Data Scientist do?
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its largelibrary of mathematical functions.
Learn more at: https://www.simplilearn.com
Data Science, Machine Learning and Neural NetworksBICA Labs
Lecture briefly overviewing state of the art of Data Science, Machine Learning and Neural Networks. Covers main Artificial Intelligence technologies, Data Science algorithms, Neural network architectures and cloud computing facilities enabling the whole stack.
This document summarizes Michał Łopuszyński's presentation on using an agile approach based on the CRISP-DM methodology for data mining projects. It discusses the key phases of CRISP-DM including business understanding, data understanding, data preparation, modelling, evaluation, and deployment. For each phase, it provides examples of best practices and challenges, with an emphasis on spending sufficient time on data understanding and preparation, developing models with the deployment context in mind, and carefully evaluating results against business objectives.
H2O World 2015
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Winning Kaggle competitions involves getting a good score as fast as possible using versatile machine learning libraries and models like Scikit-learn, XGBoost, and Keras. It also involves model ensembling techniques like voting, averaging, bagging and boosting to improve scores. The document provides tips for approaches like feature engineering, algorithm selection, and stacked generalization/stacking to develop strong ensemble models for competitions.
Consequence of long term playing videogamesDarryl Harvey
Playing computer games excessively can lead to negative effects. It can cause social isolation as people spend more time playing games online than socializing in real life, hindering the development of social skills. Too much time playing games can also disrupt academic achievement as students are distracted and tired from long gaming sessions. Prolonged sedentary screen time is linked to health issues like obesity, and lack of sleep from late night gaming may cause serious brain damage. Excessive computer use can additionally strain eyes and potentially cause vision or hearing problems with prolonged exposure. Violent games may also desensitize players to aggression over time. Overall, unregulated game playing should be addressed, as it can contribute to long-term health and social
Machine Learning has become a must to improve insight, quality and time to market. But it's also been called the 'high interest credit card of technical debt' with challenges in managing both how it's applied and how its results are consumed.
If you are curious what is ML all about, this is a gentle introduction to Machine Learning and Deep Learning. This includes questions such as why ML/Data Analytics/Deep Learning ? Intuitive Understanding o how they work and some models in detail. At last I share some useful resources to get started.
H2O World - Top 10 Data Science Pitfalls - Mark LandrySri Ambati
H2O World 2015 - Mark Landry
Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document provides an introduction to machine learning, including:
- It discusses how the human brain learns to classify images and how machine learning systems are programmed to perform similar tasks.
- It provides an example of image classification using machine learning and discusses how machines are trained on sample data and then used to classify new queries.
- It outlines some common applications of machine learning in areas like banking, biomedicine, and computer/internet applications. It also discusses popular machine learning algorithms like Bayes networks, artificial neural networks, PCA, SVM classification, and K-means clustering.
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
Fairly Measuring Fairness In Machine LearningHJ van Veen
This document discusses various approaches for measuring and achieving fairness in machine learning models. It summarizes research on identifying discrimination from models, removing protected features, and imposing different fairness constraints. Specifically, it finds that removing a protected feature like age can decrease model performance, redundant encodings may still encode that feature, and different fairness constraints like equalized odds come at a cost to model optimization but are important to consider.
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Hacking Predictive Modeling - RoadSec 2018HJ van Veen
This document provides an overview of machine learning and predictive modeling techniques for hackers and data scientists. It discusses foundational concepts in machine learning like functionalism, connectionism, and black box modeling. It also covers practical techniques like feature engineering, model selection, evaluation, optimization, and popular Python libraries. The document encourages an experimental approach to hacking predictive models through techniques like brute forcing hyperparameters, fuzzing with data permutations, and social engineering within data science communities.
Introduction to machine learning. Basics of machine learning. Overview of machine learning. Linear regression. logistic regression. cost function. Gradient descent. sensitivity, specificity. model selection.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Scienc...Edureka!
This Edureka Decision Tree tutorial will help you understand all the basics of Decision tree. This decision tree tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn decision tree analysis along with examples.
Below are the topics covered in this tutorial:
1) Machine Learning Introduction
2) Classification
3) Types of classifiers
4) Decision tree
5) How does Decision tree work?
6) Demo in R
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
H2O World - Top 10 Deep Learning Tips & Tricks - Arno CandelSri Ambati
H2O World 2015 - Arno Candel
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Data Science Training | Data Science For Beginners | Data Science With Python...Simplilearn
This Data Science presentation will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist.
This Data Science presentation will cover the following topics:
1. What is Data Science?
2. Who is a Data Scientist?
3. What does a Data Scientist do?
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its largelibrary of mathematical functions.
Learn more at: https://www.simplilearn.com
Data Science, Machine Learning and Neural NetworksBICA Labs
Lecture briefly overviewing state of the art of Data Science, Machine Learning and Neural Networks. Covers main Artificial Intelligence technologies, Data Science algorithms, Neural network architectures and cloud computing facilities enabling the whole stack.
This document summarizes Michał Łopuszyński's presentation on using an agile approach based on the CRISP-DM methodology for data mining projects. It discusses the key phases of CRISP-DM including business understanding, data understanding, data preparation, modelling, evaluation, and deployment. For each phase, it provides examples of best practices and challenges, with an emphasis on spending sufficient time on data understanding and preparation, developing models with the deployment context in mind, and carefully evaluating results against business objectives.
H2O World 2015
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Winning Kaggle competitions involves getting a good score as fast as possible using versatile machine learning libraries and models like Scikit-learn, XGBoost, and Keras. It also involves model ensembling techniques like voting, averaging, bagging and boosting to improve scores. The document provides tips for approaches like feature engineering, algorithm selection, and stacked generalization/stacking to develop strong ensemble models for competitions.
Consequence of long term playing videogamesDarryl Harvey
Playing computer games excessively can lead to negative effects. It can cause social isolation as people spend more time playing games online than socializing in real life, hindering the development of social skills. Too much time playing games can also disrupt academic achievement as students are distracted and tired from long gaming sessions. Prolonged sedentary screen time is linked to health issues like obesity, and lack of sleep from late night gaming may cause serious brain damage. Excessive computer use can additionally strain eyes and potentially cause vision or hearing problems with prolonged exposure. Violent games may also desensitize players to aggression over time. Overall, unregulated game playing should be addressed, as it can contribute to long-term health and social
This document provides quarterly financial information for Prudential Financial, Inc. for the second quarter of 2004. Some key highlights include:
- Pre-tax adjusted operating income for the Financial Services Businesses was $638 million for the first half of 2004, up 24% from the same period in 2003.
- Net income for the Financial Services Businesses was $519 million in the second quarter of 2004, up 150% from the second quarter of 2003.
- Assets under management for the Financial Services Businesses totaled $474.2 billion as of June 30, 2004, up 16% from June 30, 2003.
CloudShare provides on-demand SharePoint environments in the cloud that allow users to quickly set up, develop, test, and demo SharePoint solutions without needing to manage their own hardware. Key features include automatically provisioning a fully functional SharePoint farm within 10 minutes, easy sharing of environments, and tools for collaboration, testing, training, and migrating solutions between cloud and production.
Osservatorio mobile social networks final reportLaura Cavallaro
This document presents a research framework for analyzing the business models of mobile-Internet 2.0 social applications. It includes a taxonomy model that classifies social applications based on their focus, as well as a conceptual framework that identifies six major user needs that social applications fulfill to create value: informational, social, entertainment, communication, self-exposure, and commercial needs. The framework was developed through a mixed-methods study including a census survey and case studies of social applications. The goal of the framework is to understand and explain how social applications create, deliver, and capture value through their business models.
Ffs bop, a business opportunity like no other 12.15.16Merlita Dela Cerna
- The life insurance industry in the US generated $1.2 trillion in premiums in 2015 and provides financial security for 75 million Americans. However, nearly a third of Americans feel they need more life insurance but are unsure about what type or how much to purchase.
- First Financial Security offers a business opportunity to become an independent agent selling indexed universal life insurance policies. This provides benefits like a death benefit, lifetime income, and living benefits. Agents can earn income from commissions on policies sold and by building a team of other agents.
- The opportunity allows agents to build their own business within the company and earn recurring income from policies sold by their team. Agents receive support through business tools, marketing, and training to help
This document discusses the limitations of traditional file-based data systems and advantages of database management systems (DBMS). Traditional file-based systems have issues like data duplication, separation of data across different applications, and incompatible file formats. A DBMS addresses these limitations by providing a centralized database that stores logically related shared data and metadata. It also provides languages to define, modify and access the database along with security features.
This document summarizes a presentation on big data trends and open data. It introduces the speaker, Jongwook Woo, and his experience in big data. It then covers topics including what is big data, Hadoop and Spark frameworks, using open data for analysis, and examples of analyzing Twitter data on AlphaGo and government airline and crime data sets.
VMworld 2015: Building a Business Case for Virtual SANVMworld
This presentation discusses building a business case for VMware Virtual SAN. It provides an overview of Virtual SAN and its benefits for customers like choice, integration, cost savings and performance. A case study is presented of how Dominos Pizza implemented Virtual SAN which resulted in roughly 40% lower costs compared to a traditional storage array. The presentation concludes by demonstrating the Virtual SAN assessment tool and various ways customers can try Virtual SAN.
Documentation management system & information databasesSyed Zaid Irshad
A document management system (DMS) is a computer system (or set of computer programs) used to track and store electronic documents and/or images of paper documents.
Dokumen tersebut membahas tentang pohon kelapa, termasuk pengertian pohon kelapa, manfaat setiap bagian pohon kelapa, dan kandungan gizi pada buah kelapa. Pohon kelapa sangat bermanfaat bagi kehidupan manusia karena hampir seluruh bagian pohonnya dapat dimanfaatkan.
El documento describe el día 16 de octubre de 2015 del autor. Por la mañana, se levantó a las 9am, se bañó y desayunó. De 14:00 a 16:00 asistió a un taller de lectura y redacción, y de 16:00 a 18:00 asistió a un taller de computación. Durante la presentación de su equipo sobre virus de sistema, tuvieron varios problemas técnicos que retrasaron su presentación. Aunque lograron presentar, la maestra les indicó que habían repetido información de otros equipos. A pesar de las d
Los valores son estándares que nos permiten justificar nuestras creencias, actitudes y acciones. Un valor es una creencia relativamente permanente sobre lo que es preferible o deseable en una situación. Los valores tienen componentes cognitivos, afectivos y de conducta. Existen valores personales e interpersonales, morales y de competencia. Los valores nos guían en la toma de posiciones sociales y políticas, y justifican nuestras evaluaciones de nosotros mismos y los demás. El sistema de valores es una organización de creencias sobre modos de conducta preferidos, ordenados
This is an introductory workshop for machine learning. Introduced machine learning tasks such as supervised learning, unsupervised learning and reinforcement learning.
Supervised learning uses labeled training data to predict outcomes for new data. Unsupervised learning uses unlabeled data to discover patterns. Some key machine learning algorithms are described, including decision trees, naive Bayes classification, k-nearest neighbors, and support vector machines. Performance metrics for classification problems like accuracy, precision, recall, F1 score, and specificity are discussed.
PyData 2015 Keynote: "A Systems View of Machine Learning" Joshua Bloom
Despite the growing abundance of powerful tools, building and deploying machine-learning frameworks into production continues to be major challenge, in both science and industry. I'll present some particular pain points and cautions for practitioners as well as recent work addressing some of the nagging issues. I advocate for a systems view, which, when expanded beyond the algorithms and codes to the organizational ecosystem, places some interesting constraints on the teams tasked with development and stewardship of ML products.
About: Dr. Joshua Bloom is an astronomy professor at the University of California, Berkeley where he teaches high-energy astrophysics and Python for data scientists. He has published over 250 refereed articles largely on time-domain transients events and telescope/insight automation. His book on gamma-ray bursts, a technical introduction for physical scientists, was published recently by Princeton University Press. He is also co-founder and CTO of wise.io, a startup based in Berkeley. Josh has been awarded the Pierce Prize from the American Astronomical Society; he is also a former Sloan Fellow, Junior Fellow at the Harvard Society, and Hertz Foundation Fellow. He holds a PhD from Caltech and degrees from Harvard and Cambridge University.
The document discusses machine learning and data science concepts. It begins with an introduction to machine learning and the machine learning process. It then provides an overview of select machine learning algorithms and concepts like bias/variance, generalization, underfitting and overfitting. It also discusses ensemble methods. The document then shifts to discussing time series, functions for manipulating time series, and laying the foundation for time series prediction and forecasting. It provides examples of applying techniques like median filtering to smooth time series data. Overall, the document provides a high-level introduction and overview of key machine learning and time series concepts.
This document provides an overview of computer vision techniques including classification and object detection. It discusses popular deep learning models such as AlexNet, VGGNet, and ResNet that advanced the state-of-the-art in image classification. It also covers applications of computer vision in areas like healthcare, self-driving cars, and education. Additionally, the document reviews concepts like the classification pipeline in PyTorch, data augmentation, and performance metrics for classification and object detection like precision, recall, and mAP.
This document provides a syllabus for the Data Analyst Nanodegree program. It outlines 7 projects that students will complete to learn data analysis skills in Python, R, SQL, and MongoDB. The projects involve analyzing San Francisco bike share data, testing psychological effects, investigating datasets, wrangling OpenStreetMap data, exploring and summarizing data in R, introductory machine learning to detect fraud, and designing effective data visualizations. Supporting lessons teach statistical techniques, data wrangling, data visualization, and A/B testing.
How Machine Learning Helps Organizations to Work More Efficiently?Tuan Yang
Data is increasing day by day and so is the cost of data storage and handling. However, by understanding the concepts of machine learning one can easily handle the excessive data and can process it in an affordable manner.
The process includes making models by using several kinds of algorithms. If the model is created precisely for certain task, then the organizations have a very wide chance of making use of profitable opportunities and avoiding the risks lurking behind the scenes.
Learn more about:
» Understanding Machine Learning Objectives.
» Data dimensions in Machine Learning.
» Fundamentals of Algorithms and Mapping from Input/Output.
» Parametric and Non-parametric Machine Learning Algorithms.
» Supervised, Unsupervised and Semi-Supervised Learning.
» Estimating Over-fitting and Under-fitting.
» Use Cases.
An Online Spark Pipeline: Semi-Supervised Learning and Automatic Retraining w...Databricks
Real-time/online machine learning is an integral piece in the machine learning landscape, particularly in regard to unsupervised learning. Areas such as focused advertising, stock price prediction, recommendation engines, network evolution and IoT streams in smart cities and smart homes are increasing in demand and scale. Continuously-updating models with efficient update methodologies, accurate labeling, feature extraction, and modularity for mixed models are integral to maintaining scalability, precision, and accuracy in high demand scenarios.
This session explores a real-time/online learning algorithm and implementation using Spark Streaming in a hybrid batch/ semi-supervised setting. It presents an easy-to-use, highly scalable architecture with advanced customization and performance optimization. Within this framework, we will examine some of the key methodologies for implementing the algorithm, including partitioning and aggregation schemes, feature extraction, model evaluation and correction over time, and our approaches to minimizing loss and improving convergence. The result is a simple, accurate pipeline that can be easily adapted and scaled to a variety of use cases.
The performance of the algorithm will be evaluated comparatively against existing implementations in both linear and logistic prediction. The session will also cover real-time uses cases of the streaming pipeline using real time-series data and present strategies for optimization and implementation to improve both accuracy and efficiency in a semi-supervised setting.
InfoEducatie - Face Recognition ArchitectureBogdan Bocse
Scaling Face Recognition with Big Data discusses how to scale machine learning for face recognition. It covers how to learn from data using techniques like convolutional neural networks and preparing data through cleaning, normalization and filtering. Defining learning objectives like classification, clustering and identification is also important. When scaling learning, techniques like using GPUs and partitioning data across servers can be effective. Common challenges like local optima and data biases must also be addressed through evaluation against benchmarks. The document outlines VisageCloud's architecture and use cases for scaling face recognition through a processing pipeline and partitioning data across application and database layers.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
The document provides an introduction to data mining and knowledge discovery. It discusses how large amounts of data are extracted and transformed into useful information for applications like market analysis and fraud detection. The key steps in the knowledge discovery process are described as data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. Common data sources, database architectures, and types of coupling between data mining systems and databases are also outlined.
This document outlines the objectives, content, evaluation, and prerequisites for a course on Knowledge Acquisition in Decision Making, which introduces students to data mining techniques and how to apply them to solve business problems using SAS Enterprise Miner and WEKA. The course covers topics such as data preprocessing, predictive modeling with decision trees and neural networks, descriptive modeling with clustering and association rules, and a project presentation. Students will be evaluated based on assignments, case studies, a project, quizzes, class participation, and a final exam.
May 2015 talk to SW Data Meetup by Professor Hendrik Blockeel from KU Leuven & Leiden University.
With increasing amounts of ever more complex forms of digital data becoming available, the methods for analyzing these data have also become more diverse and sophisticated. With this comes an increased risk of incorrect use of these methods, and a greater burden on the user to be knowledgeable about their assumptions. In addition, the user needs to know about a wide variety of methods to be able to apply the most suitable one to a particular problem. This combination of broad and deep knowledge is not sustainable.
The idea behind declarative data analysis is that the burden of choosing the right statistical methodology for answering a research question should no longer lie with the user, but with the system. The user should be able to simply describe the problem, formulate a question, and let the system take it from there. To achieve this, we need to find answers to questions such as: what languages are suitable for formulating these questions, and what execution mechanisms can we develop for them? In this talk, I will discuss recent and ongoing research in this direction. The talk will touch upon query languages for data mining and for statistical inference, declarative modeling for data mining, meta-learning, and constraint-based data mining. What connects these research threads is that they all strive to put intelligence about data analysis into the system, instead of assuming it resides in the user.
Hendrik Blockeel is a professor of computer science at KU Leuven, Belgium, and part-time associate professor at Leiden University, The Netherlands. His research interests lie mostly in machine learning and data mining. He has made a variety of research contributions in these fields, including work on decision tree learning, inductive logic programming, predictive clustering, probabilistic-logical models, inductive databases, constraint-based data mining, and declarative data analysis. He is an action editor for Machine Learning and serves on the editorial board of several other journals. He has chaired or organized multiple conferences, workshops, and summer schools, including ILP, ECMLPKDD, IDA and ACAI, and he has been vice-chair, area chair, or senior PC member for ECAI, IJCAI, ICML, KDD, ICDM. He was a member of the board of the European Coordinating Committee for Artificial Intelligence from 2004 to 2010, and currently serves as publications chair for the ECMLPKDD steering committee.
Strata San Jose 2016: Scalable Ensemble Learning with H2OSri Ambati
This document discusses scalable ensemble learning using the H2O platform. It provides an overview of ensemble methods like bagging, boosting, and stacking. The stacking or Super Learner algorithm trains a "metalearner" to optimally combine the predictions from multiple "base learners". The H2O platform and its Ensemble package implement Super Learner and other ensemble methods for tasks like regression and classification. An R code demo is presented on training ensembles with H2O.
Introduction to Mahout and Machine LearningVarad Meru
This presentation gives an introduction to Apache Mahout and Machine Learning. It presents some of the important Machine Learning algorithms implemented in Mahout. Machine Learning is a vast subject; this presentation is only a introductory guide to Mahout and does not go into lower-level implementation details.
Basic machine learning background with Python scikit-learn
This document provides an overview of machine learning and the Python scikit-learn library. It introduces key machine learning concepts like classification, linear models, support vector machines, decision trees, bagging, boosting, and clustering. It also demonstrates how to perform tasks like SVM classification, decision tree modeling, random forest, principal component analysis, and k-means clustering using scikit-learn. The document concludes that scikit-learn can handle large datasets and recommends Keras for deep learning.
This document outlines a course on knowledge acquisition in decision making, including the course objectives of introducing data mining techniques and enhancing skills in applying tools like SAS Enterprise Miner and WEKA to solve problems. The course content is described, covering topics like the knowledge discovery process, predictive and descriptive modeling, and a project presentation. Evaluation includes assignments, case studies, and a final exam.
Clinical Data Classification of alzheimer's diseaseGeorge Kalangi
The document discusses using machine learning algorithms to classify clinical data related to Alzheimer's disease. It provides an overview of the process, which includes preprocessing 60 datasets, generating a merged file with clinical dementia ratings, creating prediction datasets by mapping visit intervals, and applying classification algorithms like J48 and Naive Bayes to the preprocessed data in WEKA. The goal is to analyze the output from applying these algorithms to aid in predicting a patient's future clinical status.
This document discusses using random cut forests for machine learning tasks on streaming data. It describes how random cut forests can be used for anomaly detection, density estimation, forecasting, semi-supervised learning, attribution, and directionality. Examples are provided using taxi ridership data and ECG data to demonstrate anomaly detection and forecasting capabilities. Attribution and directionality are explained using a simulated rotating fan example.
This document discusses using Robust Random Cut Forest (RRCF) for continuous machine learning over streaming data. RRCF provides an efficient and highly scalable way to summarize streaming data and detect anomalies. It can also be used for attribution and directionality to explain anomalies, hotspot detection, classification, forecasting, missing value imputation, and anomaly detection in streaming directed graphs.
This document discusses streaming data processing and the adoption of scalable frameworks and platforms for handling streaming or near real-time analysis and processing over the next few years. These platforms will be driven by the needs of large-scale location-aware mobile, social and sensor applications, similar to how Hadoop emerged from large-scale web applications. The document also references forecasts of over 50 billion intelligent devices by 2015 and 275 exabytes of data per day being sent across the internet by 2020, indicating challenges around data of extreme size and the need for rapid processing.
This document discusses the unrealized power of data and predictive analytics. It begins by highlighting how predictive analytics can be used for forecasting, targeting customers, fraud detection, risk assessment, customer churn prediction, and price elasticity analysis. It then provides examples of predictive analytics in action in various industries like healthcare, education, law enforcement, and human resources. The document emphasizes that predictive analytics must become simpler to use and be integrated into business processes. It outlines the data science process and importance of data wrangling. Finally, it discusses Microsoft's CloudML Studio and Data Lab products for building predictive models using machine learning algorithms and analyzing customer data to predict things like equipment failures and customer churn.
Roger S. Barga discusses his experience in data science and predictive analytics projects across multiple industries. He provides examples of predictive models built for customer segmentation, predictive maintenance, customer targeting, and network intrusion prevention. Barga also outlines a sample predictive analytics project for a real estate client to predict whether they can charge above or below market rates. The presentation emphasizes best practices for building predictive models such as starting small, leveraging third-party tools, and focusing on proxy metrics that drive business outcomes.
This document discusses the past, present, and future of machine learning. It outlines how machine learning has evolved from early attempts at neural networks and expert systems to today's deep learning techniques powered by large datasets and distributed computing. The document argues that machine learning and predictive analytics will be core capabilities that impact many industries and applications going forward, including personalized insurance, fraud detection, equipment monitoring, and more. Intelligence from machine learning will become "ambient" and help solve hard problems by extracting value from big data.
Streaming data ingestion and processing is becoming increasingly important as more data is produced continuously. Kinesis Streams provides a scalable and durable way to ingest streaming data into AWS. The Kinesis Client Library (KCL) makes it easy to build continuous data processing applications on top of Kinesis Streams by handling worker management, shard assignment, and fault tolerance. Kinesis Streams is enabling new classes of real-time applications and services that can process data continuously as it is produced.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
2. Deriving Knowledge from Data at Scale
Read this… A brilliant read that offers
an accessible overview of predictive
analytics, technical but at the same
time a recreational read with ample
practical examples, and it provides
footnotes for further study...
I highly recommend it…
3. Deriving Knowledge from Data at Scale
Review of Course Plan…
W5: Clustering Review
Clustering Assignment
W6: Feature Select/Create
SVMs & Regression
Data Prep Assignment
Kaggle Contest HW
W7: SVMs Cont’d
4. Deriving Knowledge from Data at Scale
• Opening Discussion 30 minutes
Review Discussion…
• Data ScienceHands On 60 minutes
• Break 5 minutes
• Data Science Modelling 30 minutes
Model performance evaluation…
• Machine Learning Boot Camp ~60 minutes
Clustering, k-Means…
• Close
5. Deriving Knowledge from Data at Scale
• Clustering
• Clustering in Weka
• Class Imbalance
• Performance Measures
6. Deriving Knowledge from Data at Scale
• Opening Discussion 30 minutes
Review Discussion…
• Data ScienceHands On 60 minutes
• Break 5 minutes
• Data Science Modelling 30 minutes
Model performance evaluation…
• Machine Learning Boot Camp ~60 minutes
Clustering, k-Means…
• Close
7. Deriving Knowledge from Data at Scale
To keep your sensor cheap and simple, you
need to sense as few of these attributes as
possible to meet the 95% requirement.
Question: Which attributes should your
sensor be capable of measuring?
11. Deriving Knowledge from Data at Scale
Began October 2006
http://www.wired.com/business/2009/09/how-the-netflix-prize-was-won/, a light read (highly suggested)
13. Deriving Knowledge from Data at Scale
from http://www.research.att.com/~volinsky/netflix/
However, improvement slowed…
14. Deriving Knowledge from Data at Scale
The top team posted a 8.5% improvement.
Ensemble methods are the best performers…
15. Deriving Knowledge from Data at Scale
“Thanks to Paul Harrison's collaboration, a
simple mix of our solutions improved our result
from 6.31 to 6.75”
Rookies
16. Deriving Knowledge from Data at Scale
“My approach is to combine the results of many
methods (also two-way interactions between
them) using linear regression on the test set.
The best method in my ensemble is regularized
SVD with biases, post processed with kernel
ridge regression”
Arek Paterek
http://rainbow.mimuw.edu.pl/~ap/ap_kdd.pdf
17. Deriving Knowledge from Data at Scale
“When the predictions of multiple RBM models and
multiple SVD models are linearly combined, we
achieve an error rate that is well over 6% better than
the score of Netflix’s own system.”
U of Toronto
http://www.cs.toronto.edu/~rsalakhu/papers/rbmcf.pdf
19. Deriving Knowledge from Data at Scale
“Our common team blends the result of team
Gravity and team Dinosaur Planet.”
Might have guessed from the name…
When Gravity and
Dinosaurs Unite
20. Deriving Knowledge from Data at Scale
And, yes, the top team which is from AT&T…
“Our final solution (RMSE=0.8712) consists
of blending 107 individual results. “
BellKor / KorBell
21. Deriving Knowledge from Data at Scale
Clustering
Fundamental Concepts: Calculating similarity of objects described
by data; Using similarity for prediction; Clustering as similarity-
based segmentation.
Exemplary Techniques: Searching for similar entities; Nearest
neighbor methods; Clustering methods; Distance metrics for
calculating similarity.
23. Deriving Knowledge from Data at Scale
Customers
Movies
I loved this movie…
The movies I watched…
You might want to
watch this movie…
You might like this one too…
25. Deriving Knowledge from Data at Scale
We may want to retrieve similar things directly. For example, IBM wants to find companies
that are similar to their best business customers, in order to have sales staff look at them as
prospects. Hewlett-Packard maintains many high performance servers for clients; this
maintenance is aided by a tool that, given a server configuration, retrieves information on
other similarly configured servers.
We may want to group similar items together into clusters, for example to see whether our
customer base contains groups of similar customers and what these groups have in
common.
Reasoning from similar cases of course extends beyond business applications; it is natural
to fields such as medicine and law. A doctor may reason about a new difficult case by
recalling a similar case and its diagnosis. A lawyer often argues cases by citing legal
precedents, which are similar historical cases whose dispositions were previously judged and
entered into the legal casebook.
28. Deriving Knowledge from Data at Scale
grouping within a group are
similar and different from (or unrelated to)
the objects in other groups
Inter-cluster
distances are
maximized
Intra-cluster
distances are
minimized
29. Deriving Knowledge from Data at Scale
• Outliers objects that do not belong to any cluster
outlier analysis
cluster
outliers
30. Deriving Knowledge from Data at Scale
data reduction
natural clusters useful outlier detection
31. Deriving Knowledge from Data at Scale
d(x, y) x y metric
• d(i, j) 0 non-negativity
• d(i, i) = 0 isolation
• d(i, j) = d(j, i) symmetry
• d(i, j) ≤ d(i, h)+d(h, j) triangular inequality
real,
boolean, categorical, ordinal
66. Deriving Knowledge from Data at Scale
Note, some implementations of K-means
only allow numerical values so it may be
necessary to convert categorical to binary.
Also, normalize attributes on very differently
scales (age and income).
70. Deriving Knowledge from Data at Scale
Some final takeaways from this model: The power of clustering and Nearest
Neighbor becomes obvious when we talk about data sets like Netflix and
Amazon. Amazon with their ~100 million users and Netflix with their 4 Billion
streamed moves, their algorithms are very accurate since there are likely many
potential customers in their databases with similar buying/viewing habits to
you. Thus, the nearest neighbor to yourself is likely very similar. This creates an
accurate and effective model.
Contrarily, the model breaks down quickly and becomes inaccurate when you
have few data points for comparison. In the early stages of an online e-
commerce store for example, when there are only 50 customers, a product
recommendation feature will likely not be accurate at all, as the nearest
neighbor may in fact be very distant from yourself.
93. Deriving Knowledge from Data at Scale
No Prob Target CustID Age
1 0.97 Y 1746 …
2 0.95 N 1024 …
3 0.94 Y 2478 …
4 0.93 Y 3820 …
5 0.92 N 4897 …
… … … …
99 0.11 N 2734 …
100 0.06 N 2422
Use a model to assign score (probability) to each instance
Sort instances by decreasing score
Expect more targets (hits) near the top of the list
3 hits in top 5% of
the list
If there 15 targets
overall, then top 5
has 3/15=20% of
targets
94. Deriving Knowledge from Data at Scale
40% of responses for
10% of cost
Lift factor = 4
80% of responses for
40% of cost
Lift factor = 2
Model
Random
100. Deriving Knowledge from Data at Scale
Once you can compute precision and recall, you are often able to produce
precision/recall curves. Suppose that you are attempting to identify spam. You
run a learning algorithm to make predictions on a test set. But instead of just
taking a “yes/no” answer, you allow your algorithm to produce its confidence.
For instance, using a perceptron, you might use the distance from the
hyperplane as a confidence measure. You can then sort all of your test emails
according to this ranking. You may put the most spam-like emails at the top
and the least spam-like emails at the bottom
101. Deriving Knowledge from Data at Scale
Once you can compute precision and recall, you are often able to produce precision/recall curves. Suppose
that you are attempting to identify spam. You run a learning algorithm to make predictions on a test set. But
instead of just taking a “yes/no” answer, you allow your algorithm to produce its confidence. For instance, using
a perceptron, you might use the distance from the hyperplane as a confidence measure. You can then sort all
of your test emails according to this ranking. You may put the most spam-like emails at the top and the least
spam-like emails at the bottom
Once you have this sorted list, you can choose how aggressively you want your
spam filter to be by setting a threshold anywhere on this list. One would hope
that if you set the threshold very high, you are likely to have high precision (but
low recall). If you set the threshold very low, you’ll have high recall (but low
precision). By considering every possible place you could put this threshold,
you can trace out a curve of precision/recall values, like the one in Figure 4.15.
This allows us to ask the question: for some fixed precision, what sort of
recall can I get…
102. Deriving Knowledge from Data at Scale
Sometimes we want a single number that informs us of the quality of the
solution. A popular way to combe precision and recall into a single number is
by taking their harmonic mean. This is known as the balanced f-measure:
The reason to use a harmonic mean rather than an arithmetic mean is that it
favors systems that achieve roughly equal precision and recall. In the extreme
case where P = R, then F = P = R. But in the imbalanced case, for instance P =
0.1 and R = 0.9, the overall f-measure is a modest 0.18.
103. Deriving Knowledge from Data at Scale
depend crucially on which class is considered
not the case that precision on the flipped task is equal to recall
on the original task
113. Deriving Knowledge from Data at Scale
blue dominates red and green
neither red nor green dominate the other
You could get the best of the red and
green curves by making a hybrid
classifier that switches between
strategies at the cross-over points.
114. Deriving Knowledge from Data at Scale
Suppose you have a test for Alzheimer’s whose false
positive rate can be varied from 5% to 25% as the
false negative rate varies from 25% to 5% (suppose
linear dependences on both):
You try the test on a population of 10,000 people, 1%
of whom actually are Alzheimer’s positive:
115. Deriving Knowledge from Data at Scale
Area under the
ROC curve =
AUC
• Area under the ROC curve (AUC) is a
measure of the model performance
0.5 𝑟𝑎𝑛𝑑𝑜𝑚 𝑚𝑜𝑑𝑒𝑙 <
𝐴𝑈𝐶 <
1 𝑝𝑒𝑟𝑓𝑒𝑐𝑡 𝑚𝑜𝑑𝑒𝑙
• Larger the AUC, better is the model
118. Deriving Knowledge from Data at Scale
to impact…
1. Build our predictive model in WEKA Explorer;
2. Use our model to score (predict) which new customers to
target in our upcoming advertising campaign;
• ARFF file manipulation (hacking), all too common pita…
• Excel manipulation to join model output with our customers list
3. Compute the lift chart to assess business impact of our
predictive model on the advertising campaign
• How are Lift charts built, of all the charts and/or performance
measures from a model this one is ‘on you’ to construct;
• Where is the business ‘bang for the buck’?
122. Deriving Knowledge from Data at Scale
Bagging
with replacement…
Boosting
Decision Trees:
bagging
boosting
123. Deriving Knowledge from Data at Scale
Decision Trees and Decision Forests
A forest is an ensemble of trees. The trees are all slightly different from one another.
terminal (leaf) node
internal
(split) node
root node0
1 2
3 4 5 6
7 8 9 10 11 12 13 14
A general tree structure
Is top
part blue?
Is bottom
part green?
Is bottom
part blue?
A decision tree
124. Deriving Knowledge from Data at Scale
Decision Forest Model: the randomness model
1) Bagging (randomizing the training set)
The full training set
The randomly sampled subset of training data made available for the tree t
Forest training
125. Deriving Knowledge from Data at Scale
Decision Forest Model: the randomness model
The full set of all possible node test parameters
For each node the set of randomly sampled features
Randomness control parameter.
For no randomness and maximum tree correlation.
For max randomness and minimum tree correlation.
2) Randomized node optimization (RNO)
Small value of ; little tree correlation. Large value of ; large tree correlation.
The effect of
Node weak learner
Node test params
Node training
126. Deriving Knowledge from Data at Scale
Decision Forest Model: training and information gain
Beforesplit
Information gain
Shannon’s entropy
Node training
(for categorical, non-parametric distributions)
Split1Split2
128. Deriving Knowledge from Data at Scale
Classification Forest
Training data in feature space
?
?
?
Entropy of a discrete distribution
with
Classification tree
training
Obj. funct. for node j (information gain)
Training node j
Output is categorical
Input data point
Node weak learner
Predictor model (class posterior)
Model specialization for classification
( is feature response)
(discrete set)
129. Deriving Knowledge from Data at Scale
Classification Forest: the weak learner model
Node weak learner
Node test params
Splitting data at node j
Weak learner: axis aligned Weak learner: oriented line Weak learner: conic section
Examples of weak learners
Feature response
for 2D example.
With a generic line in homog. coordinates.
Feature response
for 2D example.
With a matrix representing a conic.
Feature response
for 2D example.
In general may select only a very small subset of features
With or
130. Deriving Knowledge from Data at Scale
Classification Forest: the prediction model
What do we do at the leaf?
leaf
leaf
leaf
Prediction model: probabilistic
131. Deriving Knowledge from Data at Scale
Classification Forest: the ensemble model
Tree t=1 t=2 t=3
Forest output probability
The ensemble model
132. Deriving Knowledge from Data at Scale
Training different trees in the forest
Testing different trees in the forest
(2 videos in this page)
Classification Forest: effect of the weak learner model
Parameters: T=200, D=2, weak learner = aligned, leaf model = probabilistic
• “Accuracy of prediction”
• “Quality of confidence”
• “Generalization”
Three concepts to keep in mind:
Training points
133. Deriving Knowledge from Data at Scale
Training different trees in the forest
Testing different trees in the forest
Classification Forest: effect of the weak learner model
Parameters: T=200, D=2, weak learner = linear, leaf model = probabilistic
Training points
134. Deriving Knowledge from Data at Scale
Classification Forest: effect of the weak learner model
Training different trees in the forest
Testing different trees in the forest
Parameters: T=200, D=2, weak learner = conic, leaf model = probabilistic
(2 videos in this page)
Training points
135. Deriving Knowledge from Data at Scale
Classification Forest: with >2 classes
Training different trees in the forest
Testing different trees in the forest
Parameters: T=200, D=3, weak learner = conic, leaf model = probabilistic
(2 videos in this page)
Training points
136. Deriving Knowledge from Data at Scale
Classification Forest: effect of tree depth
max tree depth, D
overfittingunderfitting
T=200, D=3, w. l. = conic T=200, D=6, w. l. = conic T=200, D=15, w. l. = conic
Predictor model = prob.(3 videos in this page)
Training points: 4-class mixed
137. Deriving Knowledge from Data at Scale
Classification Forest: analysing generalization
Parameters: T=200, D=13, w. l. = conic, predictor = prob.
(3 videos in this page)
Training points: 4-class spiral Training pts: 4-class spiral, large gaps Tr. pts: 4-class spiral, larger gapsTestingposteriors
139. Deriving Knowledge from Data at Scale
Feature extraction and selection are the most important but
underrated step of machine learning. Better features are
better than better algorithms…