000			04000nam a22003017a 4500
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005			20230122184229.0
008			230122b |||||||| |||| 00| 0 eng d
020			_a9781783982042
040			_c0
082			_a519.502 YU
092			_20
100			_aChiu, Yu-Wei
245			_aMachine learning with R cookbook : _bexplore over 110 recipes to analyze data and build predictive models with the simple and easy-to-use R code _cYu-Wei Chiu
260			_aBirmingham, England _bPackt Publishing _c2015
300			_a442 pages _billustrations _c30 cm
490			_3Community experience distilled
500			_a "Quick answers to common problems"--Cover Includes index
505			_aTable of Contents Preface Chapter 1: Practical Machine Learning with R Introduction Downloading and installing R Downloading and installing RStudio Installing and loading packages Reading and writing data Using R to manipulate data Applying basic statistics Visualizing data Getting a dataset for machine learning Chapter 2: Data Exploration with RMS Titanic Introduction Reading a Titanic dataset from a CSV file Converting types on character variablesDetecting missing values Imputing missing values Exploring and visualizing data Predicting passenger survival with a decision tree Validating the power of prediction with a confusion matrix Assessing performance with the ROC curve Chapter 3: R and Statistics Introduction Understanding data sampling in R Operating a probability distribution in R Working with univariate descriptive statistics in R Performing correlations and multivariate analysis Operating linear regression and multivariate analysisConducting an exact binomial test Performing student's t-test Performing the Kolmogorov-Smirnov test Understanding the Wilcoxon Rank Sum and Signed Rank test Working with Pearson's Chi-squared test Conducting a one-way ANOVA Performing a two-way ANOVA Chapter 4: Understanding Regression Analysis Introduction Fitting a linear regression model with lm Summarizing linear model fits Using linear regression to predict unknown values Generating a diagnostic plot of a fitted model ""Fitting a polynomial regression model with lm""""Fitting a robust linear regression model with rlm""; ""Studying a case of linear regression on SLID data""; ""Applying the Gaussian model for generalized linear regression""; ""Applying the Poisson model for generalized linear regression""; ""Applying the Binomial model for generalized linear regression""; ""Fitting a generalized additive model to data""; ""Visualizing a generalized additive model""; ""Diagnosing a generalized additive model""; ""Chapter 5: Classification (I) � Tree, Lazy, and Probabilistic""; ""Introduction"" Preparing the training and testing datasetsBuilding a classification model with recursive partitioning trees Visualizing a recursive partitioning tree Measuring the prediction performance of a recursive partitioning tree Pruning a recursive partitioning tree Building a classification model with a conditional inference tree Visualizing a conditional inference tree Measuring the prediction performance of a conditional inference tree Classifying data with the k-nearest neighbor classifier Classifying data with logistic regression
520			_aIf you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.
650			_aMATHEMATICS Applied
650			_aMATHEMATICS Probability & Statistics General
650			_a R (Computer program language)
650			_aR (Langage de programmation)
843			_aPhotocopy
887			_2CamTech Library
942			_2ddc _cBK _n0
999			_c813 _d813