Feature Engineering For Machine Learning

Feature Engineering for Machine Learning PDF
Author: Alice Zheng
Publisher: "O'Reilly Media, Inc."
ISBN: 1491953195
Size: 74.63 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 218
View: 692

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Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques

Feature Engineering For Machine Learning And Data Analytics

Feature Engineering for Machine Learning and Data Analytics PDF
Author: Guozhu Dong
Publisher: CRC Press
ISBN: 1351721267
Size: 73.97 MB
Format: PDF, Docs
Category : Business & Economics
Languages : en
Pages : 400
View: 1072

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Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

The Art Of Feature Engineering

The Art of Feature Engineering PDF
Author: Pablo Duboue
Publisher: Cambridge University Press
ISBN: 1108571646
Size: 76.97 MB
Format: PDF
Category : Computers
Languages : en
Pages :
View: 3626

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When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

Python Feature Engineering Cookbook

Python Feature Engineering Cookbook PDF
Author: Soledad Galli
Publisher: Packt Publishing Ltd
ISBN: 1789807824
Size: 35.69 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 372
View: 4296

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Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries Book Description Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems. What you will learn Simplify your feature engineering pipelines with powerful Python packages Get to grips with imputing missing values Encode categorical variables with a wide set of techniques Extract insights from text quickly and effortlessly Develop features from transactional data and time series data Derive new features by combining existing variables Understand how to transform, discretize, and scale your variables Create informative variables from date and time Who this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.

Einf Hrung In Machine Learning Mit Python

Einf  hrung in Machine Learning mit Python PDF
Author: Andreas C. Müller
Publisher: O'Reilly
ISBN: 3960101120
Size: 65.59 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : de
Pages : 378
View: 4399

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Machine Learning ist zu einem wichtigen Bestandteil vieler kommerzieller Anwendungen und Forschungsprojekte geworden, von der medizinischen Diagnostik bis hin zur Suche nach Freunden in sozialen Netzwerken. Um Machine-Learning-Anwendungen zu entwickeln, braucht es keine großen Expertenteams: Wenn Sie Python-Grundkenntnisse mitbringen, zeigt Ihnen dieses Praxisbuch, wie Sie Ihre eigenen Machine-Learning-Lösungen erstellen. Mit Python und der scikit-learn-Bibliothek erarbeiten Sie sich alle Schritte, die für eine erfolgreiche Machine-Learning-Anwendung notwendig sind. Die Autoren Andreas Müller und Sarah Guido konzentrieren sich bei der Verwendung von Machine-Learning-Algorithmen auf die praktischen Aspekte statt auf die Mathematik dahinter. Wenn Sie zusätzlich mit den Bibliotheken NumPy und matplotlib vertraut sind, hilft Ihnen dies, noch mehr aus diesem Tutorial herauszuholen. Das Buch zeigt Ihnen: - grundlegende Konzepte und Anwendungen von Machine Learning - Vor- und Nachteile weit verbreiteter maschineller Lernalgorithmen - wie sich die von Machine Learning verarbeiteten Daten repräsentieren lassen und auf welche Aspekte der Daten Sie sich konzentrieren sollten - fortgeschrittene Methoden zur Auswertung von Modellen und zum Optimieren von Parametern - das Konzept von Pipelines, mit denen Modelle verkettet und Arbeitsabläufe gekapselt werden - Arbeitsmethoden für Textdaten, insbesondere textspezifische Verarbeitungstechniken - Möglichkeiten zur Verbesserung Ihrer Fähigkeiten in den Bereichen Machine Learning und Data Science Dieses Buch ist eine fantastische, super praktische Informationsquelle für jeden, der mit Machine Learning in Python starten möchte – ich wünschte nur, es hätte schon existiert, als ich mit scikit-learn anfing! Hanna Wallach, Senior Researcher, Microsoft Research

Feature Engineering Made Easy

Feature Engineering Made Easy PDF
Author: Sinan Ozdemir
Publisher: Packt Publishing Ltd
ISBN: 1787286479
Size: 62.81 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 316
View: 2308

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A perfect guide to speed up the predicting power of machine learning algorithms Key Features Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing data insights with the help of this Guide Grasp powerful feature-engineering techniques and build machine learning systems Book Description Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. What you will learn Identify and leverage different feature types Clean features in data to improve predictive power Understand why and how to perform feature selection, and model error analysis Leverage domain knowledge to construct new features Deliver features based on mathematical insights Use machine-learning algorithms to construct features Master feature engineering and optimization Harness feature engineering for real world applications through a structured case study Who this book is for If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.

Maschinelles Lernen

Maschinelles Lernen PDF
Author: Ethem Alpaydin
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110617897
Size: 16.40 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : de
Pages : 655
View: 2024

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Das maschinelle Lernen ist zwangsläufi g eines der am schnellsten wachsenden Gebiete der Computerwissenschaft. Nicht nur die zu verarbeitenden Datenmengen werden immer umfangreicher, sondern auch die Theorie, wie man sie verarbeiten und in Wissen verwandeln kann. Maschinelles Lernen ist ein verständlich geschriebenes Lehrbuch, welches ein breites Spektrum an Themen aus verschiedenen Bereichen abdeckt, wie zum Beispiel Statistik, Mustererkennung, neuronale Netze, künstliche Intelligenz, Signalverarbeitung, Steuerung und Data Mining. Darüber hinaus beinhaltet das Buch auch Themen, die von einführenden Werken häufi g nicht behandelt werden. Unter anderem: Überwachtes Lernen; Bayessche Entscheidungstheorie; parametrische und nichtparametrische Statistik; multivariate Analysis; Hidden-Markow-Modelle; bestärkendes Lernen; Kernel-Maschinen; graphische Modelle; Bayes-Schätzung und statistischen Testmethoden. Da maschinelles Lernen eine immer größere Rolle für Studierende der Informatik spielt, geht die zweite Aufl age des Buches auf diese Veränderung ein und unterstützt gezielt Anfänger in diesem Gebiet, unter anderem durch Übungsaufgaben und zusätzlichen Beispieldatensätzen. Prof. Dr. Ethem Alpaydin, Bogaziçi University, Istanbul.

Practical Data Science With Jupyter

Practical Data Science with Jupyter PDF
Author: Prateek Gupta
Publisher: BPB Publications
ISBN: 9389898064
Size: 30.58 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 360
View: 3213

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Solve business problems with data-driven techniques and easy-to-follow Python examples KEY FEATURES ● Essential coverage on statistics and data science techniques. ● Exposure to Jupyter, PyCharm, and use of GitHub. ● Real use-cases, best practices, and smart techniques on the use of data science for data applications. DESCRIPTION This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms. WHAT YOU WILL LEARN ● Rapid understanding of Python concepts for data science applications. ● Understand and practice how to run data analysis with data science techniques and algorithms. ● Learn feature engineering, dealing with different datasets, and most trending machine learning algorithms. ● Become self-sufficient to perform data science tasks with the best tools and techniques. WHO THIS BOOK IS FOR This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples. TABLE OF CONTENTS 1. Data Science Fundamentals 2. Installing Software and System Setup 3. Lists and Dictionaries 4. Package, Function, and Loop 5. NumPy Foundation 6. Pandas and DataFrame 7. Interacting with Databases 8. Thinking Statistically in Data Science 9. How to Import Data in Python? 10. Cleaning of Imported Data 11. Data Visualization 12. Data Pre-processing 13. Supervised Machine Learning 14. Unsupervised Machine Learning 15. Handling Time-Series Data 16. Time-Series Methods 17. Case Study-1 18. Case Study-2 19. Case Study-3 20. Case Study-4 21. Python Virtual Environment 22. Introduction to An Advanced Algorithm - CatBoost 23. Revision of All Chapters’ Learning

Causal Inference Modeling For Feature Engineering Of Qsar Machine Learning Models

Causal Inference Modeling for Feature Engineering of QSAR Machine Learning Models PDF
Author: Bernard Quy Nguyen
Publisher:
ISBN:
Size: 12.58 MB
Format: PDF, Docs
Category :
Languages : en
Pages :
View: 2146

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Molecular descriptors are commonly used to digitally represent the physical structure of a molecule in quantitative structure-activity relationship machine learning models, but the overwhelming abundance of features can negatively impact the model's predictive performance. This research explores causal inference modeling as a method of feature engineering for ensemble-based machine learning techniques, and evaluates model performance against other common methods of feature evaluation.

Incorporating Automated Feature Engineering Routines Into Automated Machine Learning Pipelines

Incorporating Automated Feature Engineering Routines Into Automated Machine Learning Pipelines PDF
Author: Wesley J. Runnels
Publisher:
ISBN:
Size: 53.83 MB
Format: PDF, Mobi
Category :
Languages : en
Pages : 62
View: 4388

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Automating the construction of consistently high-performing machine learning pipelines has remained difficult for researchers, especially given the domain knowledge and expertise often necessary for achieving optimal performance on a given dataset. In particular, the task of feature engineering, a key step in achieving high performance for machine learning tasks, is still mostly performed manually by experienced data scientists. In this thesis, building upon the results of prior work in this domain, we present a tool, rl_feature_eng, which automatically generates promising features for an arbitrary dataset. In particular, this tool is specically adapted to the requirements of augmenting a more general auto-ML framework. We discuss the performance of this tool in a series of experiments highlighting the various options available for use, and finally discuss its performance when used in conjunction with Alpine Meadow, a general auto-ML package.

Feature Engineering And Selection

Feature Engineering and Selection PDF
Author: Max Kuhn
Publisher: CRC Press
ISBN: 1351609467
Size: 47.26 MB
Format: PDF, ePub
Category : Business & Economics
Languages : en
Pages : 298
View: 1634

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The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

Hands On Feature Engineering With Python

Hands On Feature Engineering with Python PDF
Author: Sahiba Chopra
Publisher:
ISBN:
Size: 52.74 MB
Format: PDF, ePub
Category :
Languages : en
Pages :
View: 2355

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A hands-on course to speed up the predicting power of machine learning algorithms About This Video Get expert knowledge on different future engineering techniques on different datasets Explore feature engineering techniques used in numerical datasets Uncover and execute feature extraction popular and useful techniques Build an ensemble model based on a feature engineered dataset In Detail Feature engineering is the most important aspect of machine learning. You know that every day you put off learning the process, you are hurting your model's performance. Studies repeatedly prove that feature engineering can be much more powerful than the choice of algorithms. Yet the field of feature engineering can seem overwhelming and confusing. This course offers you the single best solution. In this course, all of the recommendations have been extensively tested and proven on real-world problems. You'll find everything included: the recommendations, the code, the data sources, and the rationale. You'll get an over-the-shoulder, step-by-step approach for every situation, and each segment can stand alone, allowing you to jump immediately to the topics most important to you. By the end of the course, you'll have a clear, concise path to feature engineering and will enable you to get improved results by applying feature engineering techniques on your datasets Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Hands-On-Feature-Engineering-with-Python . If you require support please email: [email protected]