R语言机器学习+2

内容简介

《R语言机器学3版（影印版）》提供了具备良好可读的实践指南，帮助你将机器学于实际问题。无论你是经验丰富的R语音用户还是刚接触这门语言的新手，从Brett Lantz这里都可以学到发掘关键见解、做出新的预测并可视化你的发现所需的一切。

目录

Preface Chapter I-Introducing Machine The origins of machine learning Uses and abuses of machine learning Machine learning successes The Iimits of machine Iearning Machine learning ethics How machines Iearn Data storage Abstraction GeneraIizatiOn evaluation Machine learning in practice Types ofinput data Types of machine learning algorithms Matching input data to algorithms Machine learning with R Installing R packages Loading and unloading R packages Installing RStudio Summary Chapter 2-Managing and Understanding Data R data structures Vectors Factors Lists Data frames Matrices and arrays Managi ng data with R Saving，loading，and removing R data structures importing and saving data frOm CSV files Exploring and understanding data Exploring the structure of data Exploring numeric variables Measuring the central tendency-mean and median Measuring spread—-quartiles and the five-number summary Visualizing numeric variableoxplots Visualizing numeric variables-histograms Understanding numeric data—uniform and normal distributions Measuring spread-variance and standard deviation Exploring categorical variables Measuring the central tendency-the mode Exploring relationshipetween variables Visualizing relationships-scatterplots Examining relationships-two--way cross_·tabulations Summary Chapter 3-Lazy Learning-Classification Using Nearest Neiors Understanding nearest neior classification The k．NN algorithm Measuring similarity with distance Choosing an appropriate k Preparing data for use with k-NN Why is the k-NN algorithm lazy? Example—diagnosing breast cancer with the k-NN algorithm Step 1-collecting data Step 2-exploring and preparing the data Transformation-normalizing numeric data Data preparation-creating training and test datasets Step 3-training a modeI on the data Step 4-evaluating modeI performance Step 5-improving model performance Transformation-Z．． standardization Testing alternative values of k Summary Chapter 4-Probabilistic Learning-—Classification Using …… Chapter 5-Divide and Conquer-Classification Using Decision Chapter 6-Forecasting Numeric Data-Regression Methods Chapter 7-Black Box Methods-Neural Newworks and Support Chapter 8-Flnding Patterns-Market Basket Analysis Using Chapter 9-Finding Groups of Data-Clustering with k-means Chapter 10-evaluationg Model Perforance Chapter 11-Improving Model Performance Chapter 12-Speizad Machine Learning Topics Other Books You Enjoy Index