Mastering Machine Learning with R - Second Edition by Lesmeister Cory

Author:Lesmeister, Cory [Lesmeister, Cory]
Language: eng
Format: azw3
Publisher: Packt Publishing
Published: 2017-04-24T04:00:00+00:00

Data understanding and preparation

To start, we will load these four packages. The data is in the MASS package:

> library(caret)

> library(MASS)

> library(neuralnet)

> library(vcd)

The neuralnet package will be used for the building of the model and caret for the data preparation. The vcd package will assist us in data visualization. Let's load the data and examine its structure:

> data(shuttle)

> str(shuttle)

'data.frame':256 obs. of 7 variables:

$ stability: Factor w/ 2 levepicels "stab","xstab": 2 2 2 2 2 2 2

2 2 2 ...

$ error : Factor w/ 4 levels "LX","MM","SS",..: 1 1 1 1 1 1 1 1

1 1 ...

$ sign : Factor w/ 2 levels "nn","pp": 2 2 2 2 2 2 1 1 1 1 ...

$ wind : Factor w/ 2 levels "head","tail": 1 1 1 2 2 2 1 1 1 2

...

$ magn : Factor w/ 4 levels "Light","Medium",..: 1 2 4 1 2 4 1

2 4 1 ...

$ vis : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1

...

$ use : Factor w/ 2 levels "auto","noauto": 1 1 1 1 1 1 1 1 1

1 ...

The data consists of 256 observations and 7 variables. Notice that all of the variables are categorical and the response is use with two levels, auto and noauto. The covariates are as follows:

stability: This is stable positioning or not (stab/xstab)

error: This is the size of the error (MM / SS / LX)

sign: This is the sign of the error, positive or negative (pp/nn)

wind: This is the wind sign (head / tail)

magn: This is the wind strength (Light / Medium / Strong / Out of Range)

vis: This is the visibility (yes / no)

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