Question

What is the purpose of splitting the dataset into X and y data?

1) This separates out the labels from the indicators- we are separating the independent and dependent variables.
2) No matter how the code is written, any data being entered into the algorithm must be stored in variables with the name X or y.
3) The entire dataset is too large to be entered into a training algorithm.
4) We can only see the shape of the dataset after it has been split.

Answer 1

The purpose of splitting the dataset into X and y is to separate out the independent and dependent variables.

Step-by-step explanation:

The purpose of splitting the dataset into X and y is to separate out the independent and dependent variables. The independent variable, also known as the predictor variable, is the variable that is controlled or manipulated. It is represented by the X data. The dependent variable, also known as the response variable, is the variable that changes with or depends on the value of the independent variable. It is represented by the y data. By splitting the dataset into X and y, we can analyze the relationship between the variables and build models to predict the value of the dependent variable based on the independent variable.