Split the data

These examples will use a subset of the Student Performance Dataset from UCI ML Dataset Repository.

The goal of this exercise is to predict a student's final Mathematics grade based on the following variables: sex, age, address, studytime (weekly study time), schoolsup (extra educational support), famsup (family educational support), paid (extra paid classes within the course subject) and absences.

The response is final_grade (numeric: from 0 to 20, output target).

After initial exploration, split the data into training, validation, and test sets. In this chapter, we will introduce the idea of a validation set, which can be used to select a "best" model from a set of competing models.

In Chapter 1, we demonstrated a simple way to split the data into two pieces using the sample() function. In this exercise, we will take a slightly different approach to splitting the data that allows us to split the data into more than two parts (here, we want three: train, validation, test). We still use the sample() function, but instead of sampling the indices themselves, we will assign each row to either the training, validation or test sets according to a probability distribution.

The dataset grade is already in your workspace.

Take a look at the data using the str() function.
Set a seed (for reproducibility) and then sample n_train rows to define the set of training set indices.
- Draw a sample of size nrow(grade) from the number 1 to 3 (with replacement). You want approximately 70% of the sample to be 1 and the remaining 30% to be equally split between 2 and 3.
Subset grade using the sample you just drew so that indices with the value 1 are in grade_train, indices with the value 2 are in grade_valid, and indices with 3 are in grade_test.

Exercise

Split the data

Instructions