Financial Risk Manager Part 1

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.