classification model buyer’s guide
We spent many hours on research to finding classification model, reading product features, product specifications for this guide. For those of you who wish to the best classification model, you should not miss this article. classification model coming in a variety of types but also different price range. The following is the top 8 classification model by our suggestions:
1. Statistical Regression and Classification: From Linear Models to Machine Learning (Chapman & Hall/CRC Texts in Statistical Science)
Description
Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression:
* A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods.
* Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case.
* In view of the voluminous nature of many modern datasets, there is a chapter on Big Data.
* Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems.
* Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics.
* More than 75 examples using real data.
The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis.
Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.
2. Anatomical Model Classification of Diseases
3. Mixture Model-Based Classification
Description
"This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some time. The discussion of mixtures with heavy tails and asymmetric distributions will place this text as the authoritative, modern reference in the mixture modeling literature." (Douglas Steinley, University of Missouri)
Mixture Model-Based Classification is the first monograph devoted to mixture model-based approaches to clustering and classification. This is both a book for established researchers and newcomers to the field. A history of mixture models as a tool for classification is provided and Gaussian mixtures are considered extensively, including mixtures of factor analyzers and other approaches for high-dimensional data. Non-Gaussian mixtures are considered, from mixtures with components that parameterize skewness and/or concentration, right up to mixtures of multiple scaled distributions. Several other important topics are considered, including mixture approaches for clustering and classification of longitudinal data as well as discussion about how to define a cluster
Paul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics. His research focuses on the use of mixture model-based approaches for classification, with particular attention to clustering applications, and he has published extensively within the field. He is an associate editor for several journals and has served as a guest editor for a number of special issues on mixture models.
4. 36 Bright Color Air Dry Super Light Clay Craft Kit Modeling Clay Artist Studio Toy, Great Gift for Kids
Feature
Package: You can get 36 colors clay with 3 pcs crafting kits. Weight: About 432g / 15 oz in total.Safty Material: Non-toxic, Eco-Friendly, Brightly-Colored Clay.
Ideal for beginners to make small ornaments, model animals, characters etc.
Air dry clay is extremely soft and flexible, easy for kids to shape, air dries completely in 24 hours at least. No Need Baking!
Notice: Please put the slime back in the container when you don't use it, it will be air dried if you exposure it to the air for a long time.
Description
Weight: About 432g / 15 oz in total.5. Interest Rate Models - Theory and Practice: With Smile, Inflation and Credit (Springer Finance)
Feature
Used Book in Good ConditionDescription
The 2nd edition of this successful book has several new features. The calibration discussion of the basic LIBOR market model has been enriched considerably, with an analysis of the impact of the swaptions interpolation technique and of the exogenous instantaneous correlation on the calibration outputs. A discussion of historical estimation of the instantaneous correlation matrix and of rank reduction has been added, and a LIBOR-model consistent swaption-volatility interpolation technique has been introduced.
The old sections devoted to the smile issue in the LIBOR market model have been enlarged into a new chapter. New sections on local-volatility dynamics, and on stochastic volatility models have been added, with a thorough treatment of the recently developed uncertain-volatility approach.
Examples of calibrations to real market data are now considered.
The fast-growing interest for hybrid products has led to a new chapter. A special focus here is devoted to the pricing of inflation-linked derivatives.
The three final new chapters of this second edition are devoted to credit.
Since Credit Derivatives are increasingly fundamental, and since in the reduced-form modeling framework much of the technique involved is analogous to interest-rate modeling, Credit Derivatives -- mostly Credit Default Swaps (CDS), CDS Options and Constant Maturity CDS - are discussed, building on the basic short rate-models and market models introduced earlier for the default-free market. Counterparty risk in interest rate payoff valuation is also considered, motivated by the recent Basel II framework developments.
6. Breyer Harper - 2016 Horse of The Year - Classics Model Doll
Feature
Fourth in the Horse of the Year series, this limited edition pinto, Harper, is exclusively available in 2016!The Pinto breed has several different classifications due to the diverse bloodlines that contribute to the breed: stock, hunter, pleasure, saddle and utility
Pintos can range from miniatures and ponies to horses with draft blood, and they are used for almost every discipline
Harper has a tobiano coat pattern, which means her coat has large overlapping spots of color on a white base
Model is 1:12 Scale and part of the Breyer Classics Series
Description
Fourth in the Horse of the Year series, this limited edition pinto, Harper, is exclusively available in 2016! The Pinto breed has several different classifications due to the diverse bloodlines that contribute to the breed: stock, hunter, pleasure, saddle and utility. This means that Pintos can range from miniatures and ponies to horses with draft blood, and they are used for almost every discipline! Beautiful and flashy, all Pintos have colorful coats with either overo or tobiano patterns. Harper has a tobiano coat pattern, which means her coat has large overlapping spots of color on a white base.7. Advanced Machine Learning with scikit-learn - Training DVD
Feature
Learn Advanced Machine Learning with scikit-learn from a professional trainer from your own desk.Visual training method, offering users increased retention and accelerated learning
Breaks even the most complex applications down into simplistic steps.
Comes with Extensive Working Files
Description
Number of Videos: 4 hours - 46 lessonsShips on: DVD-ROM
User Level: Advanced
Works On: Windows 7,Vista,XP- Mac OS X
In this Advanced Machine Learning with scikit-learn training course, expert author Andreas Mueller will teach you how to choose and evaluate machine learning models. This course is designed for users that already have experience with Python. You will start by learning about model complexity, overfitting and underfitting. From there, Andreas will teach you about pipelines, advanced metrics and imbalanced classes, and model selection for unsupervised learning. This video tutorial also covers dealing with categorical variables, dictionaries, and incomplete data, and how to handle text data. Finally, you will learn about out of core learning, including the sci-learn interface for out of core learning and kernel approximations for large-scale non-linear classification. Once you have completed this computer based training course, you will have learned everything you need to know to be able to choose and evaluate machine learning models. Working files are included, allowing you to follow along with the author throughout the lessons.
8. Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning (Integrated Series in Information Systems)
Description
This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems.
The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
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