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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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DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: SUPPORT VECTOR MACHINE, LOGISTIC REGRESSION, DISCRIMINANT ANALYSIS and DECISION TREES

Vergelijkbare producten zoals DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: SUPPORT VECTOR MACHINE, LOGISTIC REGRESSION, DISCRIMINANT ANALYSIS and DECISION TREES

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

for Regression and Classification Using R is ideal for a one-semester upper-level undergraduate and/or beginning level graduate course;

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Bayesian Methods for Nonlinear Classification and Regression

methods. * Focuses on the problems of classification and regression using flexible, data-driven approaches.* Demonstrates how Bayesian ideas can;

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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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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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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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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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Machine Learning and Deep Learning Using Python and TensorFlow

working professionals at all math skill levels. You will discover how to handle data, regression and logistic regression, decision trees, cross;

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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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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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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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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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Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit's;

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Machine Learning with scikit-learn Quick Start Guide

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;

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Machine Learning with Scala Quick Start Guide

covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression;

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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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Regression Analysis with R

Simple linear regression Deal with interaction, collinearity and other problems using multiple linear regression Understand diagnostics and what;

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Training Systems Using Python Statistical Modeling

classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and;

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An Introduction to Categorical Data Analysis

methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic;

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Hands-On Machine Learning with ML.NET

Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity;

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Business Analytics

using Decision Tree approaches such as CHAID (Chi-square Automatic Interaction Detection) and other classification and regression trees;

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Machine Learning Using TensorFlow Cookbook

models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google's machine;

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Learning Predictive Analytics with R

will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression;

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