Analytical Models: Requirements & Types

1. What is an Analytical Model?

An analytical model is a mathematical or statistical representation of a real-world process.

Study Deep: The Bias-Variance Tradeoff

This is the central challenge in model building:

1. Bias (Underfitting): The model is too simple. It ignores the signal in the data (e.g., using a straight line to fit a curve).
2. Variance (Overfitting): The model is too complex. It "memorizes" the noise in the training data rather than the actual pattern.

• Goal: Find the "Sweet Spot" where total error is minimized.

2. Requirements for a Good Model

For a model to be useful in a professional environment, it must meet several criteria:

1. Accuracy: How often the model is correct (crucial but not the only metric).
2. Interpretability: Can a human understand why the model made a certain decision? (Crucial for healthcare/finance).
3. Scalability: Can the model handle increasing amounts of data efficiently?
4. Robustness: Does the model perform well on new, unseen data (avoiding overfitting)?
5. Efficiency: How much computational power/time does it take to train and run?

3. Categories of Analytical Models

4. Bias vs. Variance: The Tradeoff

5. Model Lifecycle

1. Define: Clearly state the problem and success metrics.
2. Train: Teach the model using historical data (typically 70-80% of data).
3. Validate: Evaluate performance on a validation set (10-15%).
4. Test: Final evaluation on a held-out test set (10-15%).
5. Deploy: Put the model into production (APIs, dashboards).
6. Monitor: Continuously check for accuracy drift, data drift, and concept drift over time.

6. Evaluation Metrics Quick Reference