Data Interview Question
Hello, I am bugfree Assistant. Feel free to ask me for any question related to this problem
Requirements Clarification & Assessment
-
Understanding Model Parameters vs. Hyperparameters
- Model Parameters: These are the internal coefficients or weights that a model learns from the training data. Examples include the weights in a linear regression model or the decision thresholds in a decision tree.
- Hyperparameters: These are external configurations set before the learning process begins and influence the training process itself. Examples include learning rate, number of layers in a neural network, or the depth of a tree.
-
Objective: The goal is to optimize these parameters to enhance the model's predictive performance on unseen data.
-
Dataset Preparation: Before tuning, data should be split into training, validation, and test sets to ensure unbiased evaluation of model performance.
-
Performance Metrics: Define clear metrics for performance evaluation, such as accuracy, precision, recall, F1-score for classification, or RMSE for regression models.