How to split data into train set and test set in R?
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# How to split data into train set and test set in R?

This recipe helps you split data into train set and test set in R

0

## Recipe Objective

In supervides learning alogorithms such as Linear Regression, Logistic Regression and Decision Trees, tt is very crucial to split the data into training and testing sets. We first train the model using the observations in the traning dataset and then use this model to predict from testing dataset. ​

The purpose of splitting is to avoid overfitting i.e. paying attention to minor details/noise which are not necessary and only optimizes the training dataset accuracy. In the end, we need a model which can perform well on unseen data so we keep use the test data in the very end to test the trained model performance. ​

In this recipe, you will learn how to split the data into training and testing dataset. ​

## Reading the data and importing required packages

We use "caTools" package to get the required function "sample.split()" to split the dataset. ​

``` # installing and loading catools package install.packages("caTools") library(caTools) # creating a dataframe after reading the data from a csv file data_1 = read.csv("R_232_Data_1.csv") head(data_1) ```
```Cost	Weight	Weight1	Length	Height	Width
242	23.2	25.4	30.0	11.5200	4.0200
290	24.0	26.3	31.2	12.4800	4.3056
340	23.9	26.5	31.1	12.3778	4.6961
363	26.3	29.0	33.5	12.7300	4.4555
430	26.5	29.0	34.0	12.4440	5.1340
450	26.8	29.7	34.7	13.6024	4.9274
```

## STEP 2: Splitting the dataset into Train and test data

We use sample.split() and subset() function to do so.

Syntax: sample.split(Y = , SplitRatio = )

Where:

1. Y = target variable
2. SplitRatio = no of train observation divided by the total number of test observation. for eg. SplitRatio for 70%:30% (Train:Test) is 0.7. The observations are chosen randomly.
``` ind = sample.split(Y = data_1\$Cost, SplitRatio = 0.7) #subsetting into Train data train = data_1[ind,] #subsetting into Test data test = data_1[!ind,] ```

Now, checking the dimensions of the train and test data created so check whether this worked or not

``` dim(train) dim(test) ```
```111	6
48	6
```

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