Section 1
Defining the Problem
Preview
Preview: Problem Framing and Use Case Selection
- 4 of 14 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- Recognizing ML-Suitable Problems
- Defining a Measurable Prediction Target
- Grounding LLM Responses With Retrieval
- Evaluating Imbalanced Classification Models
Overview
The exam tests the critical first steps in any machine learning project: defining the problem and ensuring it's a suitable candidate for ML. A poorly defined problem leads to wasted effort and ineffective solutions, regardless of model sophistication. Understanding when ML is appropriate, how to frame the prediction target, and how to handle data limitations are essential for success.
Core Concepts
- **ML-Suitable Problems:** ML excels at identifying complex patterns across many variables where rules-based systems struggle. Consider if the problem involves interactions between numerous factors that are difficult to encode manually.
- **Measurable Prediction Targets:** Define the prediction target in concrete, measurable business terms. For example, instead of ‘improve customer satisfaction,’ define ‘reduce churn within 30 days.’ Ambiguity here leads to misaligned models and evaluation.
- **Grounding LLMs with Retrieval:** When using Large Language Models (LLMs), grounding them with approved documents and retrieval mechanisms is crucial for accuracy and auditability. This ensures responses are based on trusted sources.
Exam Tips
- Always start by questioning whether ML is truly necessary. A 'no' answer is sometimes the correct one.
- Prioritize defining a clear, measurable prediction target *before* any modeling work begins.