Answer: Training data is used to build and finetune the model, validation data is used to evaluate the model's performance during the training process, and test data is used to evaluate the final performance of a ML model.

## Explanation In machine learning model development, the data is divided into three distinct sub-samples with specific purposes: ### Training Data - **Purpose**: Used to build and fine-tune the model - **Function**: The model learns to make predictions by adjusting its parameters based on input and output data - **Role**: Initial phase where the model develops its predictive capabilities ### Validation Data - **Purpose**: Used to evaluate the model's performance during the training process - **Function**: Helps in tuning hyperparameters and determining when to stop training to prevent overfitting - **Role**: Provides feedback during model development without being part of the training data ### Test Data - **Purpose**: Used to evaluate the final performance of the machine learning model - **Function**: Provides an unbiased estimate of the model's ability to generalize to unseen data - **Role**: Ultimate assessment of model performance on data never seen during training or validation ### Why Other Options Are Incorrect: - **Choice A**: Incorrectly assigns training data for evaluation and validation data for building/fine-tuning - **Choice C**: Incorrectly uses training data for final evaluation and validation data for building/fine-tuning - **Choice D**: Incorrectly assigns training data for issue identification and test data for building/fine-tuning This three-way split ensures proper model development, prevents overfitting, and provides reliable performance assessment.

Author: Tanishq Prabhu