In this talk, Dmitry shares his approach to feature engineering which he used successfully in various Kaggle competitions. He covers common techniques used to convert your features into numeric representation used by ML algorithms.
Feature Engineering - Getting most out of data for predictive modelsGabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
In this talk, Dmitry shares his approach to feature engineering which he used successfully in various Kaggle competitions. He covers common techniques used to convert your features into numeric representation used by ML algorithms.
Feature Engineering - Getting most out of data for predictive modelsGabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
발표자: 이활석(NAVER)
발표일: 2017.11.
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨 지고 있습니다. 본 과정에서는 비지도학습의 가장 대표적인 방법인 오토인코더의 모든 것에 대해서 살펴보고자 합니다. 차원 축소관점에서 가장 많이 사용되는Autoencoder와 (AE) 그 변형 들인 Denoising AE, Contractive AE에 대해서 공부할 것이며, 데이터 생성 관점에서 최근 각광 받는 Variational AE와 (VAE) 그 변형 들인 Conditional VAE, Adversarial AE에 대해서 공부할 것입니다. 또한, 오토인코더의 다양한 활용 예시를 살펴봄으로써 현업과의 접점을 찾아보도록 노력할 것입니다.
1. Revisit Deep Neural Networks
2. Manifold Learning
3. Autoencoders
4. Variational Autoencoders
5. Applications
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
Feature Engineering in Machine LearningKnoldus Inc.
In this Knolx we are going to explore Data Preprocessing and Feature Engineering Techniques. We will also understand what is Feature Engineering and its importance in Machine Learning. How Feature Engineering can help in getting the best results from the algorithms.
Presentation in Vietnam Japan AI Community in 2019-05-26.
The presentation summarizes what I've learned about Regularization in Deep Learning.
Disclaimer: The presentation is given in a community event, so it wasn't thoroughly reviewed or revised.
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
Feature engineering — HJ Van Veen (Nubank) @@PAPIs Connect — São Paulo 2017PAPIs.io
Feature engineering is one of the most important, yet elusive, skills to master if you want to be a good data scientist. Machine learning competitions are hardly ever won with strong modeling techniques alone -- it is the combination of creative feature engineering and powerful modeling techniques that makes the difference. This tutorial will give the audience practical tips and tricks to improve the performance of machine learning algorithms. We will broadly look at feature engineering for applied machine learning, touching on subjects like: categorical vs. numerical variables, data cleaning, feature extraction, transformations, and imputation.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
발표자: 이활석(NAVER)
발표일: 2017.11.
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨 지고 있습니다. 본 과정에서는 비지도학습의 가장 대표적인 방법인 오토인코더의 모든 것에 대해서 살펴보고자 합니다. 차원 축소관점에서 가장 많이 사용되는Autoencoder와 (AE) 그 변형 들인 Denoising AE, Contractive AE에 대해서 공부할 것이며, 데이터 생성 관점에서 최근 각광 받는 Variational AE와 (VAE) 그 변형 들인 Conditional VAE, Adversarial AE에 대해서 공부할 것입니다. 또한, 오토인코더의 다양한 활용 예시를 살펴봄으로써 현업과의 접점을 찾아보도록 노력할 것입니다.
1. Revisit Deep Neural Networks
2. Manifold Learning
3. Autoencoders
4. Variational Autoencoders
5. Applications
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
Feature Engineering in Machine LearningKnoldus Inc.
In this Knolx we are going to explore Data Preprocessing and Feature Engineering Techniques. We will also understand what is Feature Engineering and its importance in Machine Learning. How Feature Engineering can help in getting the best results from the algorithms.
Presentation in Vietnam Japan AI Community in 2019-05-26.
The presentation summarizes what I've learned about Regularization in Deep Learning.
Disclaimer: The presentation is given in a community event, so it wasn't thoroughly reviewed or revised.
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
Feature engineering — HJ Van Veen (Nubank) @@PAPIs Connect — São Paulo 2017PAPIs.io
Feature engineering is one of the most important, yet elusive, skills to master if you want to be a good data scientist. Machine learning competitions are hardly ever won with strong modeling techniques alone -- it is the combination of creative feature engineering and powerful modeling techniques that makes the difference. This tutorial will give the audience practical tips and tricks to improve the performance of machine learning algorithms. We will broadly look at feature engineering for applied machine learning, touching on subjects like: categorical vs. numerical variables, data cleaning, feature extraction, transformations, and imputation.
Docker Meetup - Melbourne 2015 - Kubernetes Deep DiveKen Thompson
Presentation given at the October 2015 Docker Meetup in Melbourne. A deep dive in to Kubernetes networking and storage and how this is being utilised in OpenShift 3.
A look at kubeless a serverless framework on top of kubernetes. We take a look at what serverless is and why it matters then introduce kubeless which leverages Kubernetes API resources to provide a Function as a Services solution.
Microservices, Containers, Docker and a Cloud-Native Architecture in the Midd...Kai Wähner
Microservices are the next step after SOA: Services implement a limited set of functions. Services are developed, deployed and scaled independently. Continuous Integration and Continuous Delivery automate deployments. This way you get shorter time to results and increased flexibility. Containers improve these even more offering a very lightweight and flexible deployment option.
In the middleware world, you use concepts and tools such as an Enterprise Service Bus (ESB), Complex Event Processing (CEP), Business Process Management (BPM) or API Gateways. Many people still think about complex, heavyweight central brokers here. However, Microservices and containers are relevant not just for custom self-developed applications, but they are also a key requirement to make the middleware world more flexible, agile and automated.
This session discusses the requirements, best practices and challenges for creating a good Microservices architecture in the middleware world. A live demo with the open source PaaS framework CloudFoundry shows how technologies and frameworks such as Java, SOAP / REST Web Services, Jenkins and Docker are used to create an agile software development lifecycle to realize “Middleware Microservices”. It also discusses other modern cloud-native alternatives such as Kubernetes, Docker, Mesos, Mesosphere or Amazon ECS / AWS.
How to assess & hire Java developers accurately?HackerEarth
The problem arises when you want to hire developers who have proven Java skills. How do you assess them with accuracy when you have no clue how Java works or have never worked in it?
Have you ever wondered how to speed up your code in Python? This presentation will show you how to start. I will begin with a guide how to locate performance bottlenecks and then give you some tips how to speed up your code. Also I would like to discuss how to avoid premature optimization as it may be ‘the root of all evil’ (at least according to D. Knuth).
Machine Learning with ML.NET and Azure - Andy CrossAndrew Flatters
ML.NET is an open source, machine learning framework built in .NET and runs on Windows, Linux and macOS. It allows developers to integrate custom machine learning into their applications without any prior expertise in developing or tuning machine learning models. Enhance your .NET apps with sentiment analysis, price prediction, fraud detection and more using custom models built with ML.NET
About Andy Cross
Andy Cross (@andyelastacloud) is a co-founder of Elastacloud, an Azure Insider, co-founder of the UK London Azure User Group, an Azure MVP and a Microsoft Regional Director. An international speaker, Andy has lead teams building the largest Hadoop and HDInsight specialist deployments on Azure.
His passion for embedded software and high performance compute clusters gives him a unique insight into a sphere of computation from the very small and resource constrained to the massively scalable, limitless potential of the cloud.
Learn how to achieve scale with MongoDB. In this presentation, we cover three different ways to scale MongoDB, including optimization, vertical scaling, and horizontal scaling.
I've seen projects with shiny, new code render into unmaintainable big balls of mud within 2-3 years. Multiple times. But regardless of whether it's the code base as a whole that's rotten, or whether it's just the UI and User Experience that needs a major overhaul: the question on rewrite vs refactoring will come up sooner or later. Based on years of experience, and a plethora of bad decisions cumulating into epic failures, I'll share my experience on how to have a code base that stays maintainable - even after years. After this talk, you'll have more insight into whether you should refactor or rewrite, and how to do it right from now on.
Machine learning for IoT - unpacking the blackboxIvo Andreev
Have you ever considered Machine Learning as a black box? It sounds as a kind of magic happening. Although being one among many solutions available, Azure ML has proved to be a great balance between flexibility, usability and affordable price. But how does Azure ML compare with the other ML providers? How to choose the appropriate algorithm? Do you understand the key performance indicators and how to improve the quality of your models? The session is about understanding the black box and using it for IoT workload and not only.
Amazon DynamoDB is a fully managed NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale. This talk explores DynamoDB capabilities and benefits in detail and discusses how to get the most out of your DynamoDB database. We go over schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others. We also explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including JSON document support, Streams, and more.
A Production Quality Sketching Library for the Analysis of Big DataDatabricks
In the analysis of big data there are often problem queries that don’t scale because they require huge compute resources to generate exact results, or don’t parallelize well.
Has your app taken off? Are you thinking about scaling? MongoDB makes it easy to horizontally scale out with built-in automatic sharding, but did you know that sharding isn't the only way to achieve scale with MongoDB?
In this webinar, we'll review three different ways to achieve scale with MongoDB. We'll cover how you can optimize your application design and configure your storage to achieve scale, as well as the basics of horizontal scaling. You'll walk away with a thorough understanding of options to scale your MongoDB application.
This is a summary of the sessions I attended at PASS Summit 2017. Out of the week-long conference, I put together these slides to summarize the conference and present at my company. The slides are about my favorite sessions that I found had the most value. The slides included screenshotted demos I personally developed and tested alike the speakers at the conference.
These are slides from the Dec 17 SF Bay Area Julia Users meeting [1]. Ehsan Totoni presented the ParallelAccelerator Julia package, a compiler that performs aggressive analysis and optimization on top of the Julia compiler. Ehsan is a Research Scientist at Intel Labs working on the High Performance Scripting project.
[1] http://www.meetup.com/Bay-Area-Julia-Users/events/226531171/
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
3. Feature Engineering
• Most creative aspect of Data Science.
• Treat like any other creative endeavor, like writing a
comedy show:
• Hold brainstorming sessions
• Create templates / formula’s
• Check/revisit what worked before
4. Categorical Features
• Nearly always need some treatment
• High cardinality can create very sparse data
• Difficult to impute missing
5. Onehot encoding
• One-of-K encoding on an array of length K.
• Basic method: Used with most linear algorithms
• Dropping first column avoids collinearity
• Sparse format is memory-friendly
• Most current implementations don’t gracefully treat
missing, unseen variables
7. Hash encoding
• Does “OneHot-encoding” with arrays of a fixed
length.
• Avoids extremely sparse data
• May introduce collisions
• Can repeat with different hash functions and bag result for
small bump in accuracy
• Collisions usually degrade results, but may improve it.
• Gracefully deals with new variables (eg: new user-agents)
9. Label encoding
• Give every categorical variable a unique numerical
ID
• Useful for non-linear tree-based algorithms
• Does not increase dimensionality
• Randomize the cat_var -> num_id mapping and
retrain, average, for small bump in accuracy.
11. Count encoding
• Replace categorical variables with their count in the
train set
• Useful for both linear and non-linear algorithms
• Can be sensitive to outliers
• May add log-transform, works well with counts
• Replace unseen variables with `1`
• May give collisions: same encoding, different variables
13. LabelCount encoding
• Rank categorical variables by count in train
set
• Useful for both linear and non-linear algorithms
• Not sensitive to outliers
• Won’t give same encoding to different variables
• Best of both worlds
15. Target encoding
• Encode categorical variables by their ratio of target (binary
classification or regression)
• Be careful to avoid overfit!
• Form of stacking: single-variable model which outputs average target
• Do in cross-validation manner
• Add smoothing to avoid setting variable encodings to 0.
• Add random noise to combat overfit
• When applied properly: Best encoding for both linear and non-linear
17. Category Embedding
• Use a Neural Network to create dense embeddings
from categorical variables.
• Map categorical variables in a function approximation
problem into Euclidean spaces
• Faster model training.
• Less memory overhead.
• Can give better accuracy than 1-hot encoded.
• https://arxiv.org/abs/1604.06737
19. NaN encoding
• Give NaN values an explicit encoding instead
of ignoring
• NaN-values can hold information
• Be careful to avoid overfit!
• Use only when NaN-values in train and test set are
caused by the same, or when local validation proves
it holds signal
20. NaN encoding
Sample = [NaN]
UA UA=mobile UA=tablet UA=NaN
------- --------- --------- ------
mobile 0 0 1
tablet
mobile =>
NaN
mobile
Encoded = [0, 0, 1]
21. Polynomial encoding
• Encode interactions between categorical
variables
• Linear algorithms without interactions can not solve
the XOR problem
• A polynomial kernel *can* solve XOR
• Explodes the feature space: use FS, hashing and/or
VW
23. Expansion encoding
• Create multiple categorical variables from a single variable
• Some high cardinality features, like user-agents, hold far more
information in them:
• is_mobile?
• is_latest_version?
• Operation_system
• Browser_build
• Etc.
24. Expansion encoding
Mozilla/5.0 (Macintosh; Intel Mac OS X
10_10_4) AppleWebKit/537.36 (KHTML, like
Gecko) Chrome/53.0.2785.143 Safari/537.36
|
v
UA1 UA2 UA3 UA4 UA5
------ ------------- ------- --- -------
Chrome 53.0.2785.143 Desktop Mac 10_10_4
25. Consolidation encoding
• Map different categorical variables to the
same variable
• Spelling errors, slightly different job descriptions,
full names vs. abbreviations
• Real data is messy, free text especially so
26. Expansion encoding
company_desc desc1 company_desc2
------------------ ----- -------------
Shell Shell Gas station
shel Shell Gas station
SHELL Shell Gas station
Shell Gasoline Shell Gas station
BP => BP Gas station
British Petr. BP Gas station
B&P BP Gas station
BP Gas Station BP Gas station
bp BP Gas station
Procter&Gamble P&G Manufacturer
27. –Andrew Ng
“Coming up with features is difficult, time-
consuming, requires expert knowledge. "Applied
machine learning" is basically feature
engineering.”
28. Numerical Features
• Can be more readily fed into algorithms
• Can constitute floats, counts, numbers
• Easier to impute missing data
29. Rounding
• Round numerical variables
• Form of lossy compression: retain most significant
features of the data.
• Sometimes too much precision is just noise
• Rounded variables can be treated as categorical
variables
• Can apply log-transform before rounding
31. Binning
• Put numerical variables into a bin and
encode with bin-ID
• Binning can be set pragmatically, by quantiles,
evenly, or use models to find optimal bins
• Can work gracefully with variables outside of
ranges seen in the train set
34. Scaling
• Scale to numerical variables into a certain
range
• Standard (Z) Scaling
• MinMax Scaling
• Root scaling
• Log scaling
35. Imputation
• Impute missing variables
• Hardcoding can be combined with imputation
• Mean: Very basic
• Median: More robust to outliers
• Ignoring: just postpones the problem
• Using a model: Can expose algorithmic bias
37. Interactions
• Specifically encodes the interactions between
numerical variables
• Try: Substraction, Addition, Multiplication, Divison
• Use: Feature selection by statistical tests, or trained
model feature importances
• Ignore: Human intuition; weird interactions can
give significant improvement!
38. –Pedro Domingos
“…some machine learning projects succeed and
some fail. What makes the difference? Easily the
most important factor is the features used.”
39. Non-linear encoding for linear algo’s
• Hardcode non-linearities to improve linear
algorithms
• Polynomial kernel
• Leafcoding (random forest embeddings)
• Genetic algorithms
• Locally Linear Embedding, Spectral Embedding, t-
SNE
40. Row statistics
• Create statistics on a row of data
• Number of NaN’s,
• Number of 0’s
• Number of negative values
• Mean, Max, Min, Skewness, etc.
41. Xavier Conort
“The algorithms we used are very standard for
Kagglers. […] We spent most of our efforts in
feature engineering. [...] We were also very
careful to discard features likely to expose us to
the risk of over-fitting our model.”
42. Temporal Variables
• Temporal variables, like dates, need better local
validation schemes (like backtesting)
• Easy to make mistakes here
• Lots of opportunity for major improvements
43. Projecting to a circle
• Turn single features, like day_of_week, into
two coordinates on a circle
• Ensures that distance between max and min is the
same as min and min +1.
• Use for day_of_week, day_of_month, hour_of_day,
etc.
44. Trendlines
• Instead of encoding: total spend, encode
things like: Spend in last week, spend in last
month, spend in last year.
• Gives a trend to the algorithm: two customers with
equal spend, can have wildly different behavior —
one customer may be starting to spend more, while
the other is starting to decline spending.
45. Closeness to major events
• Hardcode categorical features like:
date_3_days_before_holidays:1
• Try: National holidays, major sport events,
weekends, first Saturday of month, etc.
• These factors can have major influence on spending
behavior.
46. Scott Locklin
“feature engineering is another topic which
doesn’t seem to merit any review papers or
books, or even chapters in books, but it is
absolutely vital to ML success. […] Much of the
success of machine learning is actually success
in engineering features that a learner can
understand.”
47. Spatial Variables
• Spatial variables are variables that encode a location
in space
• Examples include: GPS-coordinates, cities,
countries, addresses
48. Categorizing location
• Kriging
• K-means clustering
• Raw latitude longitude
• Convert cities to latitude longitude
• Add zip codes to streetnames
49. Closeness to hubs
• Find closeness between a location to a major
hub
• Small towns inherit some of the culture/context of
nearby big cities
• Phone location can be mapped to nearby businesses
and supermarkets
50. Spatial fraudulent behavior
• Location event data can be indicative of
suspicious behavior
• Impossible travel speed: Multiple simultaneous
transactions in different countries
• Spending in different town than home or shipping
address
• Never spending at the same location
52. Exploration
• Data exploration can find data health issues,
outliers, noise, feature engineering ideas,
feature cleaning ideas.
• Can use: Console, Notebook, Pandas
• Try simple stats: Min, max
• Incorporate the target so find correlation between
signal.
53. Iteration / Debugging
• Feature engineering is an iterative process:
Make your pipelines suitable for fast
iteration.
• Use sub-linear debugging: Output intermediate
information on the process, do spurious logging.
• Use tools that allow for fast experimentation
• More ideas will fail, than ideas will work
54. Label Engineering
• Can treat a label/target/dependent variable as a
feature of the data and vice versa.
• Log-transform: y -> log(y+1) | exp(y_pred) - 1
• Square-transform
• Box-Cox transform
• Create a score, to turn binary target in regression.
• Train regressor to predict a feature not available in test set.
55. Francois Chollet
“Developing good models requires iterating
many times on your initial ideas, up until the
deadline; you can always improve your models
further. Your final models will typically share
little in common with the solutions you
envisioned when first approaching the problem,
because a-priori plans basically never survive
confrontation with experimental reality.”
56. Natural Language Processing
• Can use the same ideas from categorical features.
• Deep learning (automatic feature engineering)
increasingly eating this field, but shallow learning
with well-engineered features is still competitive.
• High sparsity in data introduces you to “curse of
dimensionality”
• Many opportunities for feature engineering:
58. Cleaning
• Lowercasing: Make tokens independant of capitalisation:
“I work at NASA” -> “i work at nasa”.
• Unidecode: Convert accented characters to their ascii-
counterparts: “Memórias Póstumas de Brás Cubas” ->
“Memorias Postumas de Bras Cubas”
• Removing non-alphanumeric: Clean text by removing
anything not in [a-z] [A-Z] [0-9]. “Breaking! Amsterdam
(2009)” -> “Breaking Amsterdam 2009”
• Repairing: Fix encoding issues or trim intertoken spaces.
“C a s a C a f é” -> “Casa Café”
59. Tokenizing
• Encode punctuation marks: Hardcode “!” and “?” as tokens.
• Tokenize: Chop sentences up in word tokens.
• N-Grams: Encode consecutive tokens as tokens: “I like the
Beatles” -> [“I like”, “like the”, “the Beatles”]
• Skip-grams: Encode consecutive tokens, but skip a few: “I like
the Beatles” -> [“I the”, “like Beatles”]
• Char-grams: Same as N-grams, but character level: “Beatles” -
> [“Bea”, “eat”, “atl”, “tle”, “les”]
• Affixes: Same as char-grams, but only the postfixes and prefixes
60. Removing
• Stopwords: Remove words/tokens that appear in
stopword lists.
• Rare words: Remove words that only appear few
times in training set.
• Common words: Remove extremely common
words that may not be in a stopword list.
61. Roots
• Spelling correction: Change tokens to their
correct spelling.
• Chop: Take only the first n (8) characters of a
word.
• Stem: Reduce a word/token to its root. “cars” ->
“car”
• Lemmatize: Find semantic root “never be late” ->
“never are late”
62. Enrich
• Document features: Count number of spaces, tabs,
newlines, characters, tokens, etc.
• Entity insertion: Add more general specifications to
text “Microsoft releases Windows” -> “Microsoft
(company) releases Windows (application)”
• Parse Trees: Parse a sentence into logic form: “Alice hits
Bill” -> Alice/Noun_subject hits/Verb Bill/Noun_object.
• Reading level: Compute the reading level of a
document.
63. Similarities
• Token similarity: Count number of tokens that
appear in two texts.
• Compression distance: Look if one text can be
compressed better using another text.
• Levenshtein/Hamming/Jaccard Distance: Check
similarity between two strings, by looking at number of
operations needed to transform one in the other.
• Word2Vec / Glove: Check cosine similarity between
two averaged vectors.
64. TF-IDF
• Term Frequency: Reduces bias to long
documents.
• Inverse Document Frequency: Reduces bias to
common tokens.
• TF-IDF: Use to identify most important tokens in a
document, to remove unimportant tokens, or as a
preprocessing step to dimensionality reduction.
65. Dimensionality Reduction
• PCA: Reduce text to 50 or 100-dimensional vector.
• SVD: Reduce text to 50 or 100-dimensional vector.
• LDA: TF-IDF followed by SVD.
• LSA: Create topic vectors.
66. External models
• Sentiment Analyzers: Get a vector for negative
or positive sentiment for any text.
• Topic models: Use another dataset to create topic
vectors for a new dataset.
67. Hal Daume III
“So many papers: feature engineering is hard
and time consuming. instead here's 8 pages in
which we have to design a new weird neural net
to do the same thing”
68. Neural Networks & Deep Learning
• Neural networks claim end-to-end automatic
feature engineering.
• Feature engineering dying field?
• No! Moves the focus to architecture engineering
• And despite promise: computer vision uses features
like: HOG, SIFT, whitening, perturbation, image
pyramids, rotation, z-scaling, log-scaling, frame-
grams, external semantic data, etc.
69. Leakage / Golden Features
• Feature engineering can help exploit leakage.
• Reverse engineer:
• Reverse MD5 hash with rainbow tables.
• Reverse TF-IDF back to Term Frequency
• Encode order of samples data set.
• Encode file creation dates.
• Rule mining:
• Find simple rules (and encode these) to help your model.
70. Case Study: Quora Duplicate
Questions Dataset
• Classify ~440.000 question pairs as duplicate
or non-duplicate.
• Benchmark 1: 0.79 accuracy (Stacked Siamese
Nets)
• Benchmark 2: 0.82 accuracy (Neural Bag of
Words)
• Benchmark 3: 0.88 accuracy (Bilateral Multi-
Perspective Matching)
71. Case Study: Quora Duplicate
Questions Dataset
• First attempt: Simple bag of words with logistic
regression.
• 0.75 accuracy
• Second attempt: Polynomial feature interactions
between the tokens in both questions.
• 0.80 accuracy
72. Case Study: Quora Duplicate
Questions Dataset
• Third attempt: Use stemming with
SnowballStemmer from NLTK.
• 0.805 accuracy
• Fourth attempt: Add 2-grams
• 0.81 accuracy
73. Case Study: Quora Duplicate
Questions Dataset
• Fifth attempt: Add manual features
• Normalized difference in length between question pairs
• Normalized compression distance between question pairs.
• Cosine distance between averaged word2vec vectors for the
question pairs.
• Chargram co-occurence between question pairs.
• Token count of words: “which, what, where”
• 0.827 Accuracy
74. Case Study: Quora Duplicate
Questions Dataset
• Can you think of any more features to
engineer?
• External & pre-trained models?
• Search engine models?
• Logic based models?
75. Robert J. Bennett
This one went unusually smoothly. When I
finished it, I remarked to a friend that I felt like
an engineer who had designed a machine and
then sat back and realized it did everything I’d
set out to do.
Which made him say, quite emphatically, “No
engineer has ever felt this.”
Questions?