Visualizing probabilistic classification data in weka

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.

Data Mining with WEKA WEKA

WEKA is machine learning software written in Java that is used for data mining tasks. It contains tools for pre-processing data, building classifiers, clustering data, finding associations, attribute selection, and visualizing data. WEKA also allows users to perform experiments to compare the performance of different learning algorithms on classification and regression problems. It has graphical user interfaces that make it easy to set up and run machine learning experiments by connecting different components in a workflow.

Classification

Dr. C.V. Suresh Babu

Classification is a data analysis technique used to predict class membership for new observations based on a training set of previously labeled examples. It involves building a classification model during a training phase using an algorithm, then testing the model on new data to estimate accuracy. Some common classification algorithms include decision trees, Bayesian networks, neural networks, and support vector machines. Classification has applications in domains like medicine, retail, and entertainment.

The Analytics Frontier of the Hadoop Eco-System

inside-BigData.com

In this video from the ISC Big Data'14 Conference, Ted Willke from Intel presents: The Analytics Frontier of the Hadoop Eco-System. "The Hadoop MapReduce framework grew out of an effort to make it easy to express and parallelize simple computations that were routinely performed at Google. It wasn’t long before libraries, like Apache Mahout, were developed to enable matrix factorization, clustering, regression, and other more complex analyses on Hadoop. Now, many of these libraries and their workloads are migrating to Apache Spark because it supports a wider class of applications than MapReduce and is more appropriate for iterative algorithms, interactive processing, and streaming applications. What’s next beyond Spark? Where is big data analytics processing headed? How will data scientists program these systems? In this talk, we will explore the current analytics frontier, the popular debates, and discuss some potentially clever additions. We will also share the emergent data science applications and collaborative university research that inform our thinking." Learn more: http://www.isc-events.com/bigdata14/schedule.html and http://www.intel.com/content/www/us/en/software/intel-graph-solutions.html Watch the video presentation: https://www.youtube.com/watch?v=qlfx495Ekw0

Weka : A machine learning algorithms for data mining

Keshab Kumar Gaurav

Spark + AI Summit - The Importance of Model Fairness and Interpretability in ...

Francesca Lazzeri, PhD

Machine learning model fairness and interpretability are critical for data scientists, researchers and developers to explain their models and understand the value and accuracy of their findings. Interpretability is also important to debug machine learning models and make informed decisions about how to improve them. In this session, Francesca will go over a few methods and tools that enable you to “unpack" machine learning models, gain insights into how and why they produce specific results, assess your AI systems fairness and mitigate any observed fairness issues. Using open source fairness and interpretability packages, attendees will learn how to: - Explain model prediction by generating feature importance values for the entire model and/or individual datapoints. - Achieve model interpretability on real-world datasets at scale, during training and inference. - Use an interactive visualization dashboard to discover patterns in data and explanations at training time. - Leverage additional interactive visualizations to assess which groups of users might be negatively impacted by a model and compare multiple models in terms of their fairness and performance.

Making Netflix Machine Learning Algorithms Reliable

Justin Basilico

This document discusses making Netflix machine learning algorithms reliable. It describes how Netflix uses machine learning for tasks like personalized ranking and recommendation. The goals are to maximize member satisfaction and retention. The models and algorithms used include regression, matrix factorization, neural networks, and bandits. The key aspects of making the models reliable discussed are: automated retraining of models, testing training pipelines, checking models and inputs online for anomalies, responding gracefully to failures, and training models to be resilient to different conditions and failures.

Barga Data Science lecture 10

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.

Where Search Meets Machine Learning: Presented by Diana Hu & Joaquin Delgado,...

Lucidworks

This document discusses how machine learning problems can be framed as search-based systems and how search technologies can be leveraged to build and serve machine learning models at scale. It begins with an introduction to search and information retrieval systems. It then discusses how recommender systems and other machine learning problems can be viewed as search problems involving relevance, ranking, and retrieval. The document explores options for integrating machine learning models into search systems like Solr and Lucene using techniques like custom scoring plugins and the Predictive Model Markup Language (PMML). It provides examples of training models and exporting them to PMML for use in search systems.

Lucene/Solr Revolution 2015: Where Search Meets Machine Learning

Joaquin Delgado PhD.

Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.

Lucene/Solr Revolution 2015: Where Search Meets Machine Learning

S. Diana Hu

This document discusses how machine learning problems can be framed as search-based systems and how search and machine learning can be combined. It begins with an introduction to search engines and information retrieval. It then discusses how machine learning problems like recommender systems can be viewed as search tasks involving ranking, retrieval, and relevance calculation. The document proposes simplifying the machine learning pipeline by integrating it with search systems and indexes. It provides examples of implementing machine learning scoring and models within search systems like Solr using techniques like PMML. The goal is to leverage existing search infrastructure for scaling machine learning models.

Presentation

QA Fest 2019. Никита Кричко. Тестирование приложений, использующих ИИ

QAFest

This document provides an overview of machine learning application testing. It discusses common mistakes in data science like cherry picking and false causality. It describes different types of machine learning tasks like supervised classification and unsupervised clustering. The document outlines how to test various parts of a machine learning application including the data, model, and different phases. It provides examples of testing the boundaries, detecting outliers, and using generative adversarial networks. Finally, it discusses the role of a QA engineer in gathering data to validate a system works for non-standard situations and does not cause harm.

Introduction to Data Mining

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.

Chapter 1: Introduction to Data Mining

Christopher Sneed, MSDS, PMP, CSPO

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.

Presentation - Predicting Online Purchases Using Conversion Prediction Modeli...

- The document presents research on predicting online purchases for Homesite Group Inc. using conversion prediction modeling. - Various machine learning algorithms were tested on Homesite's dataset to predict whether an insurance quote seeker would purchase a policy, including naive Bayes, k-nearest neighbors, logistic regression, and boosting. - The models were tested on different train-test splits of Homesite's dataset containing 260,000 training and 173,000 test records. The logistic regression model achieved the highest accuracy of around 81% at predicting conversions.

Thesis Defense

Steven Han

Quantifying visual similarity is essential for modeling visual search. Different aspects play different roles in calculating visual similarity. Feature generation is the key problem to solve. The document then describes artificial neural networks and how they imitate signal transmission between neurons. It discusses an overview of visual search and similarity, feature-based quantification methods, and implementations of different feature generation methods like PCA, OBVIS, and SDA on parallel architectures like CUDA and OpenMP.

Machine Learning Interpretability - Mateusz Dymczyk - H2O AI World London 2018

Sri Ambati

This talk was recorded in London on Oct 30, 2018 and can be viewed here: https://youtu.be/p4iAnxwC_Eg The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes! This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models Mateusz is a software developer who loves all things distributed, machine learning and hates buzzwords. His favourite hobby data juggling. He obtained his M.Sc. in Computer Science from AGH UST in Krakow, Poland, during which he did an exchange at L’ECE Paris in France and worked on distributed flight booking systems. After graduation he move to Tokyo to work as a researcher at Fujitsu Laboratories on machine learning and NLP projects, where he is still currently based.

Hybrid system architecture overview

Jesse Wang

Essentials of Automations: The Art of Triggers and Actions in FME

Safe Software

In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation. We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios. Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!

GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...

Neo4j

Leonard Jayamohan, Partner & Generative AI Lead, Deloitte This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.

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