MARC details
| 000 -LEADER |
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| fixed length control field |
04000nam a22003017a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20230122184229.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230122b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781783982042 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
0 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.502 YU |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) |
|---|
| Edition number |
0 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Chiu, Yu-Wei |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Machine learning with R cookbook : |
| Remainder of title |
explore over 110 recipes to analyze data and build predictive models with the simple and easy-to-use R code |
| Statement of responsibility, etc. |
Yu-Wei Chiu |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Birmingham, England |
| Name of publisher, distributor, etc. |
Packt Publishing |
| Date of publication, distribution, etc. |
2015 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
442 pages |
| Other physical details |
illustrations |
| Dimensions |
30 cm |
| 490 ## - SERIES STATEMENT |
|---|
| Materials specified |
Community experience distilled |
| 500 ## - GENERAL NOTE |
|---|
| General note |
<br/>"Quick answers to common problems"--Cover<br/>Includes index |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Table of Contents<br/>Preface<br/>Chapter 1: Practical Machine Learning with R<br/>Introduction<br/>Downloading and installing R<br/>Downloading and installing RStudio<br/>Installing and loading packages<br/>Reading and writing data<br/>Using R to manipulate data<br/>Applying basic statistics<br/>Visualizing data<br/>Getting a dataset for machine learning<br/>Chapter 2: Data Exploration with RMS Titanic<br/>Introduction<br/>Reading a Titanic dataset from a CSV file Converting types on character variablesDetecting missing values<br/>Imputing missing values<br/>Exploring and visualizing data<br/>Predicting passenger survival with a decision tree<br/>Validating the power of prediction with a confusion matrix<br/>Assessing performance with the ROC curve<br/>Chapter 3: R and Statistics<br/>Introduction<br/>Understanding data sampling in R<br/>Operating a probability distribution in R<br/>Working with univariate descriptive statistics in R<br/>Performing correlations and multivariate analysis Operating linear regression and multivariate analysisConducting an exact binomial test<br/>Performing student's t-test<br/>Performing the Kolmogorov-Smirnov test<br/>Understanding the Wilcoxon Rank Sum and Signed Rank test<br/>Working with Pearson's Chi-squared test<br/>Conducting a one-way ANOVA<br/>Performing a two-way ANOVA<br/>Chapter 4: Understanding Regression Analysis<br/>Introduction<br/>Fitting a linear regression model with lm<br/>Summarizing linear model fits<br/>Using linear regression to predict unknown values<br/>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<br/>Visualizing a recursive partitioning tree<br/>Measuring the prediction performance of a recursive partitioning tree<br/>Pruning a recursive partitioning tree<br/>Building a classification model with a conditional inference tree<br/>Visualizing a conditional inference tree<br/>Measuring the prediction performance of a conditional inference tree<br/>Classifying data with the k-nearest neighbor classifier<br/>Classifying data with logistic regression |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
If 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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
MATHEMATICS Applied |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
MATHEMATICS Probability & Statistics General |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
R (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
R (Langage de programmation) |
| 843 ## - REPRODUCTION NOTE |
|---|
| Type of reproduction |
Photocopy |
| 887 ## - NON-MARC INFORMATION FIELD |
|---|
| Source of data |
CamTech Library |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
| Suppress in OPAC |
No |
