<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["es", "en"].<end_of_turn>
<start_of_turn>user
¿Qué es un conjunto de pruebas en aprendizaje automático?<end_of_turn>
<start_of_turn>model
In machine learning, a test set is a crucial component of the model development process. It is a subset of data used to assess the performance of a machine learning model after it has been trained. The key characteristics and purpose of a test set include:

1. Evaluation of Model Performance: The primary role of a test set is to evaluate how well the machine learning model performs on new, unseen data. It helps in estimating the model's generalization ability — its effectiveness in making predictions on data it has not encountered during training.

2. Separation from Training Data: A test set is kept separate from the training data, which is used to build and train the model. This separation is critical to ensure that the evaluation of the model's performance is unbiased and reflects its ability to generalize from the training data to new data.

3. Realistic Assessment: The test set should ideally represent the real-world data that the model will encounter in practical applications. This means the test data should have a similar distribution and characteristics as the actual data the model is intended to work with.

4. Prevention of Overfitting: Using a test set helps in identifying if the model has overfitted the training data. Overfitting occurs when a model learns the training data too well, including its noise and outliers, and consequently performs poorly on new, unseen data.

5. Model Comparison: Test sets are also used to compare the performance of different models or different versions of the same model. This comparison helps in selecting the best model for deployment.

6. Final Stage Evaluation: The test set is usually used at the end of the model development process. After a model has been trained (using the training set) and validated (using a validation set, if available), the test set provides the final performance metrics.

It's important to note that the test set should only be used once to avoid bias in the model evaluation. Continuously using the test set for model tuning can lead to inadvertent overfitting to the test data, defeating its purpose as an unbiased evaluator of model performance.<end_of_turn><eos>