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Classification and Regression Trees

have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use;

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Classification and Regression Trees

have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use;

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Machine Learning Pocket Reference Working with Structured Data in Python

(regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the;

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Predictive Analytics

Provides a foundation in classical parametric methods of regression and classification essential for pursuing advanced topics in predictive;

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Bayesian Methods for Nonlinear Classification and Regression

Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian;

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Algorithms for Regression and Classification

The focus of this dissertation is on robust regression and classification in genetic association studies. In the context of robust;

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Using Classification and Regression Trees

Classification and regression trees (CART) is one of the several contemporary statistical techniques with good promise for research in many;

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Multivariate Nonparametric Regression And Visualization

an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for;

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Ensemble Classification Methods with Applications in R

An essential guide to two burgeoning topics in machine learning - classification trees and ensemble learning Ensemble Classification;

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Statistical Modelling in Higher Learning

presents you with a solid understanding on the predictors using classification tree mining and logistic regression analysis.Classification is the;

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Statistical Regression and Classification

This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned;

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Biostatistics with R

classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing;

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Biostatistics with R

classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing;

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Data-Driven Computational Neuroscience

regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts;

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Artificial Intelligence and Machine Learning Fundamentals

regression and classification concepts to real-world problems Perform predictive analysis using decision trees and random forests Carry out;

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Data Mining and Business Analytics with R

and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical;

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Statistical Regression and Classification: From Linear Models to Machine Learning

This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned;

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Segmentation and Positioning for Strategic Marketing Decisions

, multivariate analysis, CHAID, and classification and regression trees. Other chapters deal with perceptual positioning maps-point and vector, value;

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Proactive Data Mining with Decision Trees

This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only;

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Introduction to Machine Learning with R

regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you'll delve into the;

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Credit Risk Management

markets products. The author describes not only standard rating and scoring methods like Classification Trees or Logistic Regression, but also;

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Support Vector Machines

of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization;

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Human Action Analysis with Randomized Trees

trees and the unsupervised random trees. When there are sufficient amount of labeled data available, supervised random trees provides a fast;

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Data Science Algorithms in a Week

how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What;

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Building Better Models with JMP Pro

and the methods used. Topics include: regression, logistic regression, classification and regression trees, neural networks, model cross;

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Classification Methods for Remotely Sensed Data

developments, Classification Methods for Remotely Sensed Data, Second Edition provides a comprehensive and up-to-date review of the entire field;

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