Yes, we can separate the data into train, validation and test sets. This is usually done to evaluate the performance of the model and prevent overfitting. In Python, the train_test_split function of the scikit-learn library is often used to perform this operation.
Here is an example:
from sklearn.model_selection import train_test_split
# First of all, we separate the data into train and test.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Then we separate the train set into train and validation.
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42) # 0.25
In this code, the test_size parameter determines the size of the test set. In the first stage, 80% of the data is allocated for the training set and 20% for the test set. In the second stage, 25% of the training set (that is, 20% of the original data) is reserved for the validation set. As a result, 60% of the data is used for training, 20% for validation and 20% for testing.
The random_state parameter ensures the repeatability of the process. Thanks to this parameter, we can have the same split data set every time. The value of this parameter is usually an integer, and it is entirely up to you which value you use.
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