Differences between training set and validation set

The training set and the validation set are two distinct subsets of the dataset used in machine learning for different purposes:

1. Training Set:
— The training set is the portion of the dataset used to train the machine learning model.
— It consists of examples (data points) with both input features and corresponding target labels (or outcomes).
— During the training process, the model learns from the patterns and relationships present in the training data.
— The model’s parameters are adjusted iteratively to minimize the difference between its predictions and the actual target labels in the training set.
— The training set should represent a diverse range of examples that adequately cover the variability present in the data.

2. Validation Set:
— The validation set is a separate portion of the dataset that is used to evaluate the performance of the trained model.
— It is used to assess how well the model generalizes to unseen data.
— The validation set is not used during the training process to update the model’s parameters.
— Instead, after training the model on the training set, its performance is evaluated on the validation set.
— The performance metrics calculated on the validation set (such as accuracy, precision, recall, etc.) provide insights into how well the model is expected to perform on new, unseen data.
— The validation set helps in tuning hyperparameters, selecting the best-performing model, and detecting issues such as overfitting or underfitting.

In summary, the training set is used to train the model by adjusting its parameters, while the validation set is used to evaluate the model’s performance and guide decisions related to model selection and hyperparameter tuning. It’s crucial to have separate training and validation sets to ensure an unbiased assessment of the model’s performance on unseen data.

Why not use Testing data ?

It’s not advisable to go straight to testing data for several reasons:

1. Bias in Evaluation: If you directly evaluate your model’s performance on the testing data without having a separate validation set, you risk biasing your evaluation. This is because your model’s performance on the testing data may influence decisions during the model development process, leading to overfitting to the testing data. This undermines the purpose of having a separate testing set, which is to provide an unbiased estimate of the model’s performance on unseen data.
2. Hyperparameter Tuning: During model development, you often need to tune hyperparameters to optimize the model’s performance. Using the testing data for this purpose can lead to overfitting to the testing set, again biasing your evaluation. Instead, you should use a separate validation set for hyperparameter tuning and model selection.
3. Generalization Performance: The ultimate goal of a machine learning model is to generalize well to new, unseen data. By evaluating the model’s performance on a separate validation set, you gain insight into its ability to generalize beyond the training data. This allows you to make informed decisions about the model’s performance and potential deployment in real-world scenarios.
4. Data Leakage: If you use the testing data during the model development process, there’s a risk of unintentional data leakage, where information from the testing data inadvertently influences the model. This can lead to overly optimistic performance estimates and invalidate your evaluation.

By adhering to the practice of using separate training, validation, and testing sets, you ensure a robust and unbiased evaluation of your machine learning model’s performance, thereby increasing confidence in its ability to generalize to new data.