When splitting a training set into training and validation subsets, there are several considerations that should be made to ensure an effective and unbiased evaluation of the model. Here are some important factors to consider:

  1. Size of the dataset: It is important to have a sufficient amount of data in both the training and validation sets. A commonly used split is 70% for training and 30% for validation, but this can vary depending on the size of the dataset.

  2. Randomness: The data should be randomly split into training and validation sets to minimize any biases that may be present in the data. This can be achieved by using random sampling techniques.

  3. Representative samples: The training and validation sets should be representative of the overall dataset. Care should be taken to ensure that the distribution of classes or categories is preserved in both subsets. This is particularly important in cases of imbalanced datasets where one class may dominate the data.

  4. Time-based splitting: For time-series data, it is common to split the dataset based on a specific point in time. The training set should include historical data, while the validation set should contain more recent data to simulate real-world scenarios.

  5. Consistency: If the model is trained and validated multiple times, it is essential to ensure consistent splits across each iteration. This can be achieved by setting a random seed to reproduce the same split each time.

  6. Avoid information leakage: Information leakage occurs when data from the validation set unknowingly influences the training process. This can lead to over-optimistic model performance. To avoid this, the validation set should not be used for feature selection, hyperparameter tuning, or any other aspects of model development.

  7. Cross-validation: In addition to a simple train-validation split, it is often beneficial to use cross-validation techniques such as k-fold cross-validation. This helps to further assess the model's performance by training and validating on multiple subsets of the data.

  In summary, when splitting a training set into training and validation subsets, factors such as the size of the dataset, randomness, representativeness, time-based splitting, consistency, information leakage prevention, and cross-validation should all be carefully considered to ensure an unbiased evaluation of the model's performance.