HH Data Science Meetup: Explaining complex machine learning models with LIME

•

8 j'aime•6,329 vues

This document is a graph showing interpretability on the y-axis and complexity on the x-axis. It shows that as models increase in complexity, their interpretability decreases, with more complex models being less interpretable. Simple models are highly interpretable but less complex while more complex models are less interpretable.

Technologie

interpretability
complexity
0 1
0
25
50
75
100

Contenu connexe

Tendances

Recently, Convolutional Neural Networks have been successfully applied to image segmentation tasks. Here we present some of the most recent techniques that increased the accuracy in such tasks. First we describe the Inception architecture and its evolution, which allowed to increase width and depth of the network without increasing the computational burden. We then show how to adapt classification networks into fully convolutional networks, able to perform pixel-wise classification for segmentation tasks. We finally introduce the hypercolumn technique to further improve state-of-the-art on various fine-grained localization tasks.

Modern Convolutional Neural Network techniques for image segmentation

Gioele Ciaparrone

This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.

Explainable AI in Healthcare

vonaurum

Artificial Intelligence

Jay Nagar

In this presentation we describe the formulation of the HMM model as consisting of states that are hidden that generate the observables. We introduce the 3 basic problems: Finding the probability of a sequence of observation given the model, the decoding problem of finding the hidden states given the observations and the model and the training problem of determining the model parameters that generate the given observations. We discuss the Forward, Backward, Viterbi and Forward-Backward algorithms.

An overview of Hidden Markov Models (HMM)

ananth

Deep learning: Overfitting , underfitting, and regularization

Aly Abdelkareem

Simulated Annealing

Joy Dutta

65487681 60444264-engineering-optimization-theory-and-practice-4th-edition

Ashish Arora

Cloud Computing Use Cases Whitepaper 3 0

Jason Reed

HML: Historical View and Trends of Deep Learning

Yan Xu

If then rule in fuzzy logic and fuzzy implications

Siksha 'O' Anusandhan (Deemed to be University )

Quality of Service

silenceIT Inc.

Rule based system

Dr. C.V. Suresh Babu

3.Medium Access Control

Sonali Chauhan

Fuzzy set

NilaNila16

Binary Class and Multi Class Strategies for Machine Learning

Paxcel Technologies

Constraint satisfaction problems (csp)

Archana432045

Query processing

University of Potsdam

CP Optimizer is a generic system, largely based on CP, to model and solve real-world combinatorial optimization problems with a particular focus on planning and scheduling applications. It provides an algebraic language with simple mathematical concepts (such as intervals, sequences or functions) to capture the temporal dimension of planning and scheduling problems in a combinatorial optimization framework. CP Optimizer implements a model-and-run paradigm that vastly reduces the burden on the user to understand CP or scheduling algorithms: modeling is by far the most important. The automatic search integrates a large panel of techniques from Artificial Intelligence (constraint programming, temporal reasoning, learning, ...) and Operations Research (mathematical programming, graph algorithms, local search, ...) into an exact algorithm that provides good performance out of the box and is continuously improving. This tutorial gives an overview of CP Optimizer for planning and scheduling: typical applications, modeling concepts with examples, ingredients of the automatic search, tools and performance.

Planning/Scheduling with CP Optimizer

Philippe Laborie

Markov model for the online multichannel attribution problem

adavide1982

Forward Backward Chaining

QAU ISLAMABAD,PAKISTAN

Tendances (20)

Modern Convolutional Neural Network techniques for image segmentation

Explainable AI in Healthcare

Artificial Intelligence

An overview of Hidden Markov Models (HMM)

Deep learning: Overfitting , underfitting, and regularization

Simulated Annealing

65487681 60444264-engineering-optimization-theory-and-practice-4th-edition

Cloud Computing Use Cases Whitepaper 3 0

HML: Historical View and Trends of Deep Learning

If then rule in fuzzy logic and fuzzy implications

Quality of Service

Rule based system

3.Medium Access Control

Fuzzy set

Binary Class and Multi Class Strategies for Machine Learning

Constraint satisfaction problems (csp)

Query processing

Planning/Scheduling with CP Optimizer

Markov model for the online multichannel attribution problem

Forward Backward Chaining

Plus de Shirin Elsinghorst

Transparente und Verantwortungsbewusste KI: Was bedeutet Explainable und Responsible AI und warum sollten wir uns damit beschäftigen? In dieser Session widmen wir uns den aktuellen Themen Explainable AI (XAI) und Responsible AI (RAI), bei denen es um die Entwicklung von Künstlicher Intelligenz geht, die transparent, erklärbar und ethisch verantwortlich agiert. Wir wollen ein breites Publikum ansprechen, von KI-Experten und Forschern bis hin zu Vertretern aus Wirtschaft, Politik und der Zivilgesellschaft. Ziel ist es, ein Verständnis für die Bedeutung von Explainable und Responsible AI zu schaffen, mögliche Herausforderungen zu diskutieren und Lösungsansätze für eine verantwortungsvolle KI-Nutzung zu entwickeln.

Transparente und Verantwortungsbewusste KI

Shirin Elsinghorst

Die digitale Transformation und der Aufstieg von Künstlicher Intelligenz (KI) haben die Geschäftswelt revolutioniert. Unternehmen, die ihre Geschäftsentscheidungen auf Daten und KI stützen, gewinnen einen klaren Wettbewerbsvorteil und schaffen innovative Lösungen für ihre Kunden. Diese Veranstaltung richtet sich an Führungskräfte, Manager, Unternehmer und Data-Enthusiasten, die mehr darüber erfahren möchten, ob und wann sie eine Datenstrategie brauchen, wie sie eine effektive Datenstrategie entwickeln und wie sie damit ein datengetriebenes und KI-basiertes Business aufbauen oder bereichern können.

Datenstrategie in der Praxis

Shirin Elsinghorst

Vortrag auf der M3 Online-Konferenz am 16.06.2020 (https://online.m3-konferenz.de/lecture.php?id=12337&source=0) Mit Machine Learning getroffene Entscheidungen sind inhärent schwierig – wenn nicht gar unmöglich – nachzuvollziehen. Ein scheinbar gutes Ergebnis mit Hilfe von maschinellen Lernverfahren ist oft schnell erzielt oder wird von anderen als bahnbrechend verkauft. Die Komplexität einiger der besten Modelle wie neuronaler Netze ist genau das, was sie so erfolgreich macht. Aber es macht sie gleichzeitig zu einer Black Box. Das kann problematisch sein, denn Geschäftsführer oder Vorstände werden weniger geneigt sein, einer Entscheidung zu vertrauen und nach ihr zu handeln, wenn sie sie nicht verstehen. Shapley Values, Local Interpretable Model-Agnostic Explanations (LIME) und Anchors sind Ansätze, diese komplexen Modelle zumindest teilweise nachvollziehbar zu machen. In diesem Vortrag erkläre ich, wie diese Ansätze funktionieren, und zeige Anwendungsbeispiele. LERNZIELE * Die Teilnehmer erhalten Einblick in Möglichkeit, die komplexe Modelle erklärbar machen. * Sie lernen, Datensätze kritisch zu hinterfragen und angemessen aufzuteilen. * Und sie erfahren, unter welchen Bedingungen sie Entscheidungen durch Machine Learning vertrauen können.

RICHTIG GUT: DIE QUALITÄT VON MODELLEN VERSTEHEN

Shirin Elsinghorst

Real-World Data Science (Fraud Detection, Customer Churn & Predictive Mainten...

Shirin Elsinghorst

Keras is a high-level open-source deep learning framework that by default works on top of TensorFlow. Keras is minimalistic, efficient and highly flexible because it works with a modular layer system to define, compile and fit neural networks. It has been written in Python but can also be used from within R. Because the underlying backend can be changed from TensorFlow to Theano and CNTK (with more options being developed right now) it is designed to be framework-independent. Models can be trained on CPU or GPU, locally or in the cloud. I will show an example how to build an image classifier with Keras. We'll be using a convolutional neural net to classify fruits in images. But that's not all! We not only want to judge our black-box model based on accuracy and loss measures - we want to get a better understanding of how the model works. We will use an algorithm called LIME (local interpretable model-agnostic explanations) to find out what part of the different test images contributed most strongly to the classification that was made by our model. I will introduce LIME and explain how it works. And finally, I will show how to apply LIME to the image classifier we built before, as well as to a pretrained Imagenet model. You will get: * an introduction to Keras * an overview about deep learning and neural nets * a demo how to build an image classifier with Keras * an introduction to explaining black box models, specifically to the LIME algorithm * a demo how to apply LIME to explain the predictions of our own Keras image classifier, as well as of a pretrained Imagenet Further Information: * www.shirin-glander.de<http://www.shirin-glander.de> * https://blog.codecentric.de/author/shirin-glander/ * www.youtube.com/codecentricAI

SAP webinar: Explaining Keras Image Classification Models with LIME

Shirin Elsinghorst

These are the slides from workshop: Introduction to Machine Learning with R which I gave at the University of Heidelberg, Germany on June 28th 2018. The accompanying code to generate all plots in these slides (plus additional code) can be found on my blog: https://shirinsplayground.netlify.com/2018/06/intro_to_ml_workshop_heidelberg/ The workshop covered the basics of machine learning. With an example dataset I went through a standard machine learning workflow in R with the packages caret and h2o: - reading in data - exploratory data analysis - missingness - feature engineering - training and test split - model training with Random Forests, Gradient Boosting, Neural Nets, etc. - hyperparameter tuning

Workshop - Introduction to Machine Learning with R

Shirin Elsinghorst

This is a slide deck from a presentation, that my colleague Uwe Friedrichsen (https://www.slideshare.net/ufried/) and I did together. As we created our respective parts of the presentation on our own, it is quite easy to figure out who did which part of the presentation as the two slide decks look quite different ... :) For the sake of simplicity and completeness, Uwe copied the two slide decks together. As he did the "surrounding" part, he added my part at the place where I took over and then added concluding slides at the end. Well, I'm sure, you will figure it out easily ... ;) The presentation was intended to be an introduction to deep learning (DL) for people who are new to the topic. It starts with some DL success stories as motivation. Then a quick classification and a bit of history follows before the "how" part starts. The first part of the "how" is some theory of DL, to demystify the topic and explain and connect some of the most important terms on the one hand, but also to give an idea of the broadness of the topic on the other hand. After that the second part dives deeper into the question how to actually implement DL networks. This part starts with coding it all on your own and then moves on to less coding step by step, depending on where you want to start. The presentation ends with some pitfalls and challenges that you should have in mind if you want to dive deeper into DL - plus the invitation to become part of it. As always the voice track of the presentation is missing. I hope that the slides are of some use for you, though.

Deep learning - a primer

Shirin Elsinghorst

HH Data Science Meetup: Explaining complex machine learning models with LIME

Shirin Elsinghorst

Ruhr.PY - Python Developer Meetup: Keras is a high-level API written in Python for building and prototyping neural networks. It can be used on top of TensorFlow, Theano or CNTK. In this talk we build, train and visualize a Model using Python and Keras - all interactive with Jupyter Notebooks! https://www.meetup.com/Ruhr-py/events/248093628/ -- slide deck generated with beautiful.ai -- -- video recording can be seen here: https://youtu.be/Q8hVXnpEPmc -- -- comment here: https://shirinsplayground.netlify.com/2018/04/ruhrpy_meetup_2018_slides/ --

Ruhr.PY - Introducing Deep Learning with Keras and Python

Shirin Elsinghorst

From Biology to Industry. A Blogger’s Journey to Data Science.

Shirin Elsinghorst

Plus de Shirin Elsinghorst (10)

Transparente und Verantwortungsbewusste KI

Datenstrategie in der Praxis

RICHTIG GUT: DIE QUALITÄT VON MODELLEN VERSTEHEN

Real-World Data Science (Fraud Detection, Customer Churn & Predictive Mainten...

SAP webinar: Explaining Keras Image Classification Models with LIME

Workshop - Introduction to Machine Learning with R

Deep learning - a primer

HH Data Science Meetup: Explaining complex machine learning models with LIME

Ruhr.PY - Introducing Deep Learning with Keras and Python

From Biology to Industry. A Blogger’s Journey to Data Science.

Dernier

presentation ICT roal in 21st century education

jfdjdjcjdnsjd

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Juan lago vázquez

Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Orbitshub

AWS Community Day CPH - Three problems of Terraform

Andrey Devyatkin

Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Angeliki Cooney

Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.

Corporate and higher education May webinar.pptx

Rustici Software

Exploring Multimodal Embeddings with Milvus

Zilliz

Whatsapp Number Escorts Call girls 8617370543 Available 24x7 Mcleodganj Call Girls Service Offer Genuine VIP Model Escorts Call Girls in Your Budget. Mcleodganj Call Girls Service Provide Real Call Girls Number. Make Your Sexual Pleasure Memorable with Our Mcleodganj Call Girls at Affordable Price. Top VIP Escorts Call Girls, High Profile Independent Escorts Call Girls, Housewife Women Escorts Call Girl, College Girls Escorts Call Girls, Russian Escorts Call girls Service in Your Budget.

Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model

Deepika Singh

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

sammart93

💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨‍🏫👨‍💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

UiPathCommunity

Six Myths about Ontologies: The Basics of Formal Ontology

johnbeverley2021

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

How to Troubleshoot Apps for the Modern Connected Worker

ThousandEyes

The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ 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 New York 2024 - The value of a flexible API Management solution for O...

apidays

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ 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 New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ 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 New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

apidays

Architecting Cloud Native Applications

WSO2

Artificial Intelligence Chap.5 : Uncertainty

Khushali Kathiriya

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER

MadyBayot

Dernier (20)

presentation ICT roal in 21st century education

Why Teams call analytics are critical to your entire business

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

AWS Community Day CPH - Three problems of Terraform

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Corporate and higher education May webinar.pptx

Exploring Multimodal Embeddings with Milvus

Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

Six Myths about Ontologies: The Basics of Formal Ontology

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

How to Troubleshoot Apps for the Modern Connected Worker

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

Architecting Cloud Native Applications

Artificial Intelligence Chap.5 : Uncertainty

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER