#### Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
R for Data Science Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Functions in R
Data Preprocessing and Preparation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distributions
Statistical Inference in R
Time Series Mining with R
Index

## Measuring model performance with a confusion matrix

To measure the performance of a classification model, we can first generate a classification table based on our predicted label and actual label. We then use a confusion matrix to obtain performance measures such as precision, recall, specificity, and accuracy. In this recipe, we will demonstrate how to retrieve a confusion matrix using the `caret` package.

You need to have the previous recipes completed by generating a classification model, and assign the model to the variable `fit`.

### How to do it…

Perform the following steps to generate classification measurement:

1. Predict labels using the fitted model, `fit`:

```> pred = predict(fit, testset[,! names(testset) %in% c("buy")], type="class")
```
2. Generate a classification table:

```> table(pred, testset[,c("buy")])

pred  no yes
no  11   1
yes  0   8
```
3. Lastly, generate a confusion matrix using prediction results and actual labels from the testing dataset:

`> confusionMatrix(pred, testset...`