Feature Engineering For Machine Learning

Feature Engineering for Machine Learning PDF
Author: Alice Zheng
Publisher: "O'Reilly Media, Inc."
ISBN: 1491953195
Size: 69.97 MB
Format: PDF
Category : Computers
Languages : en
Pages : 218
View: 4659

Get Book

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: 57.66 MB
Format: PDF, Docs
Category : Business & Economics
Languages : en
Pages : 400
View: 1281

Get Book

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: 1108709389
Size: 45.36 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 283
View: 948

Get Book

A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.

Python Feature Engineering Cookbook

Python Feature Engineering Cookbook PDF
Author: Soledad Galli
Publisher: Packt Publishing Ltd
ISBN: 1789807824
Size: 32.71 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 372
View: 3405

Get Book

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.

Feature Engineering Made Easy

Feature Engineering Made Easy PDF
Author: Sinan Ozdemir
Publisher: Packt Publishing Ltd
ISBN: 1787286479
Size: 49.81 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 316
View: 4831

Get Book

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.

Current Based Feature Engineering For Bearing Fault Machine Learning Algorithms

Current Based Feature Engineering for Bearing Fault Machine Learning Algorithms PDF
Author: Julián Rodríguez Pardo
Publisher:
ISBN:
Size: 40.57 MB
Format: PDF, Mobi
Category :
Languages : en
Pages :
View: 5234

Get Book

In the current work a study about possible features for fault detection in an induction motor can be used for machine learning using the supply current. For this feature engineering was done to mainly transform the current signal into the frequency signal using a fast fourier transform. In this paper it was found that the current signal is a useful feature for machine learning. However certain procesing of the data needs to be done in order to reveal the diferences between a healthy and a faulty bearing.

Automated Feature Engineering And Advanced Applications In Data Science

Automated Feature Engineering and Advanced Applications in Data Science PDF
Author: Mrutyunjaya Panda
Publisher: Engineering Science Reference
ISBN: 9781799866596
Size: 37.20 MB
Format: PDF, ePub, Mobi
Category : Automatic classification
Languages : en
Pages : 335
View: 5766

Get Book

"This edited book will start with an introduction to feature engineering and then move onto recent concepts, methods and applications with the use of various data types that includes: text, image, streaming data, social network data, financial data, biomedical data, bioinformatics etc. to help readers gain insight into how features can be extracted and transformed from raw data"--

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: 52.81 MB
Format: PDF, Kindle
Category :
Languages : en
Pages :
View: 3156

Get Book

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.

Feature Engineering And Selection

Feature Engineering and Selection PDF
Author: Max Kuhn
Publisher: CRC Press
ISBN: 1351609467
Size: 68.52 MB
Format: PDF, Mobi
Category : Business & Economics
Languages : en
Pages : 298
View: 1750

Get Book

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.

Feature Learning And Understanding

Feature Learning and Understanding PDF
Author: Haitao Zhao
Publisher: Springer Nature
ISBN: 3030407942
Size: 56.46 MB
Format: PDF
Category : Science
Languages : en
Pages : 291
View: 5931

Get Book

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.

Content Based Image Classification

Content Based Image Classification PDF
Author: Rik Das
Publisher: CRC Press
ISBN: 1000280713
Size: 39.43 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 180
View: 521

Get Book

Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques is a comprehensive guide to research with invaluable image data. Social Science Research Network has revealed that 65% of people are visual learners. Research data provided by Hyerle (2000) has clearly shown 90% of information in the human brain is visual. Thus, it is no wonder that visual information processing in the brain is 60,000 times faster than text-based information (3M Corporation, 2001). Recently, we have witnessed a significant surge in conversing with images due to the popularity of social networking platforms. The other reason for embracing usage of image data is the mass availability of high-resolution cellphone cameras. Wide usage of image data in diversified application areas including medical science, media, sports, remote sensing, and so on, has spurred the need for further research in optimizing archival, maintenance, and retrieval of appropriate image content to leverage data-driven decision-making. This book demonstrates several techniques of image processing to represent image data in a desired format for information identification. It discusses the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Image feature extraction with novel handcrafted techniques (traditional feature extraction) Image feature extraction with automated techniques (representation learning with CNNs) Significance of fusion-based approaches in enhancing classification accuracy MATLAB® codes for implementing the techniques Use of the Open Access data mining tool WEKA for multiple tasks The book is intended for budding researchers, technocrats, engineering students, and machine learning/deep learning enthusiasts who are willing to start their computer vision journey with content-based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means for insight generation. Readers will learn coding techniques necessary to propose novel mechanisms and disruptive approaches. The WEKA guide provided is beneficial for those uncomfortable coding for machine learning algorithms. The WEKA tool assists the learner in implementing machine learning algorithms with the click of a button. Thus, this book will be a stepping-stone for your machine learning journey. Please visit the author's website for any further guidance at https://www.rikdas.com/

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: 39.24 MB
Format: PDF, Kindle
Category :
Languages : en
Pages : 62
View: 4886

Get Book

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.

Party Parrots

Party Parrots PDF
Author: Rocket Baby Club
Publisher:
ISBN: 9781684549900
Size: 50.95 MB
Format: PDF, ePub
Category : Juvenile Nonfiction
Languages : en
Pages : 38
View: 2833

Get Book

Machine learning in artificial intelligence is finally accessible to kids! Patty the parrot is planning a huge party at the Party Parrots' amusement park. There are a lot of party parrots and Patty needs to know what to buy! Using the important concept of feature engineering, can you and your loved ones help Patty figure out what to buy?

Stock Price Prediction Using Feature Engineering And Machine Learning Techniques

Stock Price Prediction Using Feature Engineering and Machine Learning Techniques PDF
Author: Aditya Vijay Narkar
Publisher:
ISBN:
Size: 78.58 MB
Format: PDF, ePub
Category :
Languages : en
Pages :
View: 3180

Get Book

The correct prediction of stock prices is a challenging task, as stock prices are affected by a large number of parameters. Moreover, many of these parameters, such as investor sentiment or future market potential, cannot be measured and quantified directly, while having a substantial impact on individual stocks and the stock market as a whole. In this project, I analyzed the changes in the stock price to predict the stock's direction in the future. That is done by extracting multiple descriptors from past data and using them to predict the price change of the stock up to 100 days in the future. Experimental results are collected using 10 stocks and Random Forest, SVM, and KNN classifiers and compared against a baseline ZeroR prediction. The project's goal is to assist the stock traders by providing data-driven insights about the predicted time and direction of changes in the stock price.