Train, validation, test sets

as it mentioned when we have test data is available then we can split train-validation data in 75-25 or 70-30, i am confuses how to split in this situtation

If you have separate test data set available, divide the training set into any of these:

  1. 70/30
  2. 75/25
  3. 80/20
  4. 90/10

Keep the majority chunk for training and a small portion for validation.

Thank you for your quick reply and i got your point but my question is when we have separate test data, so then how to assign inputs & target col.

We don’t need to make split for test data and we can directly assign input-target for test set.

For example:

In case of training data we split them to form train and validation set

# Create training and validation sets
train_inputs, val_inputs, train_targets, val_targets = train_test_split(
    inputs_df[numeric_cols + encoded_cols], targets, test_size=0.25, random_state=42)
# For test set ( If we don't have target variable available )
test_input = test_df[numeric_cols + encoded_cols]
# For test set ( If target variable is available )
test_input = test_df[numeric_cols + encoded_cols]
test_target = test_df[target]

Its just for representational though, hope it helps.