Machine learning with R cookbook analyze data and build predictive models
Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code About This Book Apply R to simplify predictive modeling with short and simple code Use machine learning to solve problems ranging from small to big data Build a training and testing dataset, apply...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai, [India] :
Packt
2017.
|
| Edición: | Second edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630390206719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Customer Feedback
- Table of Contents
- Preface
- Chapter 1: Practical Machine Learning with R
- Introduction
- Downloading and installing R
- Getting ready
- How to do it...
- How it works...
- See also
- Downloading and installing RStudio
- Getting ready
- How to do it...
- How it works...
- See also
- Installing and loading packages
- Getting ready
- How to do it...
- How it works...
- See also
- Understanding of basic data structures
- Data types
- Data structures
- Vectors
- How to do it...
- How it works...
- Lists
- How to do it...
- How it works...
- Array
- How to do it...
- How it works...
- Matrix
- How to do it...
- DataFrame
- How to do it...
- Basic commands for subsetting
- How to do it...
- Data input
- Reading and writing data
- Getting ready
- How to do it...
- How it works...
- There's more...
- Manipulating data
- Getting ready
- How to do it...
- How it works...
- There's more...
- Applying basic statistics
- Getting ready
- How to do it...
- How it works...
- There's more...
- Visualizing data
- Getting ready
- How to do it...
- How it works...
- See also
- Getting a dataset for machine learning
- Getting ready
- How to do it...
- How it works...
- See also
- Chapter 2: Data Exploration with Air Quality Datasets
- Introduction
- Using air quality dataset
- Getting ready
- How to do it...
- How it works...
- There's more...
- Converting attributes to factor
- Getting ready
- How to do it...
- How it works...
- There's more...
- Detecting missing values
- Getting ready
- How to do it...
- How it works...
- There's more...
- Imputing missing values
- Getting ready
- How to do it...
- How it works...
- Exploring and visualizing data
- Getting ready.
- How to do it...
- Predicting values from datasets
- Getting ready
- How to do it...
- How it works...
- Chapter 3: Analyzing Time Series Data
- Introduction
- Looking at time series data
- Getting ready
- How to do it...
- How it works...
- See also
- Plotting and forecasting time series data
- Getting ready
- How to do it...
- How it works...
- See also
- Extracting, subsetting, merging, filling, and padding
- Getting ready
- How to do it...
- How it works...
- See also
- Successive differences and moving averages
- Getting ready
- How to do it...
- How it works...
- See also
- Exponential smoothing
- Getting ready
- How to do it...
- How it works...
- See also
- Plotting the autocorrelation function
- Getting ready
- How to do it...
- How it works...
- See also
- Chapter 4: R and Statistics
- Introduction
- Understanding data sampling in R
- Getting ready
- How to do it...
- How it works...
- See also
- Operating a probability distribution in R
- Getting ready
- How to do it...
- How it works...
- There's more...
- Working with univariate descriptive statistics in R
- Getting ready
- How to do it...
- How it works...
- There's more...
- Performing correlations and multivariate analysis
- Getting ready
- How to do it...
- How it works...
- See also
- Conducting an exact binomial test
- Getting ready
- How to do it...
- How it works...
- See also
- Performing a student's t-test
- Getting ready
- How to do it...
- How it works...
- See also
- Performing the Kolmogorov-Smirnov test
- Getting ready
- How to do it...
- How it works...
- See also
- Understanding the Wilcoxon Rank Sum and Signed Rank test
- Getting ready
- How to do it...
- How it works...
- See also
- Working with Pearson's Chi-squared test
- Getting ready
- How to do it...
- How it works.
- There's more...
- Conducting a one-way ANOVA
- Getting ready
- How to do it...
- How it works...
- There's more...
- Performing a two-way ANOVA
- Getting ready
- How to do it...
- How it works...
- See also
- Chapter 5: Understanding Regression Analysis
- Introduction
- Different types of regression
- Fitting a linear regression model with lm
- Getting ready
- How to do it...
- How it works...
- There's more...
- Summarizing linear model fits
- Getting ready
- How to do it...
- How it works...
- See also
- Using linear regression to predict unknown values
- Getting ready
- How to do it...
- How it works...
- See also
- Generating a diagnostic plot of a fitted model
- Getting ready
- How to do it...
- How it works...
- There's more...
- Fitting multiple regression
- Getting ready
- How to do it...
- How it works...
- Summarizing multiple regression
- Getting ready
- How to do it...
- How it works...
- See also
- Using multiple regression to predict unknown values
- Getting ready
- How to do it...
- How it works...
- See also
- Fitting a polynomial regression model with lm
- Getting ready
- How to do it...
- How it works...
- There's more...
- Fitting a robust linear regression model with rlm
- Getting ready
- How to do it...
- How it works...
- There's more...
- Studying a case of linear regression on SLID data
- Getting ready
- How to do it...
- How it works...
- See also
- Applying the Gaussian model for generalized linear regression
- Getting ready
- How to do it...
- How it works...
- See also
- Applying the Poisson model for generalized linear regression
- Getting ready
- How to do it...
- How it works...
- See also
- Applying the Binomial model for generalized linear regression
- Getting ready
- How to do it...
- How it works...
- See also.
- Fitting a generalized additive model to data
- Getting ready
- How to do it...
- How it works...
- See also
- Visualizing a generalized additive model
- Getting ready
- How to do it...
- How it works...
- There's more...
- Diagnosing a generalized additive model
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 6: Survival Analysis
- Introduction
- Loading and observing data
- Getting ready
- How to do it...
- How it works...
- There's more...
- Viewing the summary of survival analysis
- Getting ready
- How to do it...
- How it works...
- Visualizing the Survival Curve
- Getting ready
- How to do it...
- How it works...
- Using the log-rank test
- Getting ready
- How to do it...
- How it works...
- Using the COX proportional hazard model
- Getting ready
- How to do it...
- How it works...
- Nelson-Aalen Estimator of cumulative hazard
- Getting ready
- How to do it...
- How it works...
- See also
- Chapter 7: Classification 1 - Tree, Lazy, and Probabilistic
- Introduction
- Preparing the training and testing datasets
- Getting ready
- How to do it...
- How it works...
- There's more...
- Building a classification model with recursive partitioning trees
- Getting ready
- How to do it...
- How it works...
- See also
- Visualizing a recursive partitioning tree
- Getting ready
- How to do it...
- How it works...
- See also
- Measuring the prediction performance of a recursive partitioning tree
- Getting ready
- How to do it...
- How it works...
- See also
- Pruning a recursive partitioning tree
- Getting ready
- How to do it...
- How it works...
- See also
- Handling missing data and split and surrogate variables
- Getting ready
- How to do it...
- How it works...
- See also
- Building a classification model with a conditional inference tree.
- Getting ready
- How to do it...
- How it works...
- See also
- Control parameters in conditional inference trees
- Getting ready
- How to do it...
- How it works...
- See also
- Visualizing a conditional inference tree
- Getting ready
- How to do it...
- How it works...
- See also
- Measuring the prediction performance of a conditional inference tree
- Getting ready
- How to do it...
- How it works...
- See also
- Classifying data with the k-nearest neighbor classifier
- Getting ready
- How to do it...
- How it works...
- See also
- Classifying data with logistic regression
- Getting ready
- How to do it...
- How it works...
- See also
- Classifying data with the Naïve Bayes classifier
- Getting ready
- How to do it...
- How it works...
- See also
- Chapter 8: Classification 2 - Neural Network and SVM
- Introduction
- Classifying data with a support vector machine
- Getting ready
- How to do it...
- How it works...
- See also
- Choosing the cost of a support vector machine
- Getting ready
- How to do it...
- How it works...
- See also
- Visualizing an SVM fit
- Getting ready
- How to do it...
- How it works...
- See also
- Predicting labels based on a model trained by a support vector machine
- Getting ready
- How to do it...
- How it works...
- There's more...
- Tuning a support vector machine
- Getting ready
- How to do it...
- How it works...
- See also
- The basics of neural network
- Getting ready
- How to do it...
- Training a neural network with neuralnet
- Getting ready
- How to do it...
- How it works...
- See also
- Visualizing a neural network trained by neuralnet
- Getting ready
- How to do it...
- How it works...
- See also
- Predicting labels based on a model trained by neuralnet
- Getting ready
- How to do it...
- How it works...
- See also.
- Training a neural network with nnet.
