How to split train test dataset for machine learning in R

Splitting a dataset into a training set and a test set is an important step in machine learning. It allows to train the model on one set of data and then evaluate its performance on a separate set of data. This can help to prevent overfitting, which is when a model is too closely fit to the training data, and as a result, it performs poorly on new data.

There are several ways to split a dataset into a training set and a test set in R, without using any specific code. One common method is to randomly select a certain percentage of the data for the test set and the remaining data for the training set. Another method is to use a function or package that can split the data into a training set and a test set.

It’s important to keep in mind that the size of the test set should be big enough to provide a reliable evaluation of the model’s performance but not too big that it affects the model’s training.

 

In this Data Science Recipe, you will learn: How to split train test dataset for machine learning in R.

 

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