Classification Prediction Methodology Development PDF Download

Author:
Publisher:
ISBN:
Category : Crime
Languages : en
Pages : 12

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Book Description

Author:
Publisher:
ISBN:
Category : Criminal justice, Administration of
Languages : en
Pages :

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Author: Pieter Kubben
Publisher: Springer
ISBN: 3319997130
Category : Medical
Languages : en
Pages : 219

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Book Description
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Author: National Institute of Justice (U.S.)
Publisher:
ISBN:
Category : Crime
Languages : en
Pages : 12

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Author: Dr. Pratibha Vijay Jadhav & Dr. Vaishali Vilas Patil
Publisher: Ashok Yakkaldevi
ISBN: 1387858807
Category : Art
Languages : en
Pages : 266

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Book Description
Today’s world is bounded by data, from morning to night each and all work is associated to data. The usage of computer and its technology is rapidly growing in many different fields like Education, banking sector, bioinformatics field, business, health cares and Industry. In all ways, everywhere data is created and this information is stored in various hubs or data wares houses. There is huge amount of data and it is created by increasing usage of computer. There is rapidly growth of data generated by all systems and it can be used for deriving models by assessing useful relationship among input and output dependencies. Consequently, there is presently shifted a model since classical modelling and it investigates to develop a model and the equivalent analyses from stored data. Government organizations, scientific institutions, administration offices and businesses have all dedicated huge resources to assembly and putting away information. Now a days, Data can possibly assist organizations with improving tasks and make quicker, progressively powerful decisions. The information or data is gathered from various sources including messages, cell phones, applications, databases, servers and different methods. This information is collected, arranged, controlled and put in meaningful information. This meaningful information would assist to an organization with valuable understanding to hold the clients for expand the income and improved the business activities. The government organizations and companies are gathering the useful information to support to manage human resources.

Author: National Institute of Justice (U.S.)
Publisher:
ISBN:
Category : Crime and criminals
Languages : en
Pages : 8

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Author: Hariom Tatsat
Publisher: "O'Reilly Media, Inc."
ISBN: 1492073008
Category : Computers
Languages : en
Pages : 432

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Book Description
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Author: A. K. Md. Ehsanes Saleh
Publisher: John Wiley & Sons
ISBN: 1119625424
Category : Mathematics
Languages : en
Pages : 484

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Book Description
Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based on rank Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset selection to obtain parsimonious models with outlier-free prediction. This book is intended for statisticians, economists, biostatisticians, data scientists and graduate students. Rank-Based Methods for Shrinkage and Selection elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes: Development of rank theory and application of shrinkage and selection Methodology for robust data science using penalized rank estimators Theory and methods of penalized rank dispersion for ridge, LASSO and Enet Topics include Liu regression, high-dimension, and AR(p) Novel rank-based logistic regression and neural networks Problem sets include R code to demonstrate its use in machine learning

Author: Frank E. Harrell
Publisher: Springer Science & Business Media
ISBN: 147573462X
Category : Mathematics
Languages : en
Pages : 583

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Book Description
Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

Author: Timothy Masters
Publisher: Apress
ISBN: 1484233360
Category : Computers
Languages : en
Pages : 530

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Book Description
Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application. Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program. What You'll Learn Compute entropy to detect problematic predictors Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising Use Monte-Carlo permutation methods to assess the role of good luck in performance results Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions Who This Book is For Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.