ML Problem Framing
Learn to assess when ML is appropriate, structure solutions, and define success metrics for effective ML strategies
What is ML Problem Framing?
ML problem framing is the critical first step in any machine learning project. It involves determining whether ML is the right solution for your problem, structuring the problem appropriately, and defining clear success metrics.
According to Google's research, more ML projects fail due to poor problem framing than technical issues. Getting this step right can save months of wasted effort and resources.
Key Questions to Answer:
- • Is ML the right approach for this problem?
- • What type of ML problem is this?
- • How will we measure success?
- • What are the constraints and requirements?
When is ML Appropriate?
✅ Good Fit for ML
- • Pattern recognition: Complex patterns in data
- • Large datasets: Sufficient training data available
- • Predictive value: Clear business value in predictions
- • Automation benefits: Scale requires automation
- • Tolerance for errors: Some mistakes are acceptable
- • Changing patterns: Rules change frequently
❌ Poor Fit for ML
- • Simple rules: Problem can be solved with basic logic
- • Limited data: Insufficient training examples
- • High stakes: Zero tolerance for mistakes
- • Regulatory constraints: Requires full explainability
- • Static problems: Rules rarely change
- • Small scale: Manual approach is sufficient
Interactive Problem Assessment
Select a scenario below to see how we assess whether ML is appropriate:
Types of ML Problems
Classification Problems
Predicting categories or classes from input data.
Examples: Email spam detection, image recognition, sentiment analysis
Success Metrics: Accuracy, Precision, Recall, F1-Score
Data Requirements: Labeled examples for each class
Regression Problems
Predicting continuous numerical values from input data.
Examples: House price prediction, stock prices, demand forecasting
Success Metrics: RMSE, MAE, R-squared
Data Requirements: Historical data with target values
Clustering Problems
Grouping similar data points without predefined categories.
Examples: Customer segmentation, anomaly detection, data exploration
Success Metrics: Silhouette score, business validation
Data Requirements: Unlabeled data with relevant features
Defining Success Metrics
Business Metrics vs. ML Metrics
Business Metrics
- • Revenue impact
- • Cost reduction
- • User satisfaction
- • Operational efficiency
ML Metrics
- • Accuracy/Precision/Recall
- • Model performance
- • Training efficiency
- • Inference latency
Success Metrics Checklist
Problem Framing Template
# ML Problem Framing Template
## Business Context
Problem: [Describe the business problem you're trying to solve]
Stakeholders: [Who are the key stakeholders?]
Current Solution: [How is this problem currently being solved?]
Success Criteria: [What defines success for this project?]
## ML Assessment
ML Appropriate: [Yes/No - with reasoning]
Problem Type: [Classification/Regression/Clustering/Other]
Data Availability: [Describe available data sources]
Data Quality: [Assessment of data quality and completeness]
## Solution Design
Input Features: [What data will the model use?]
Target Variable: [What are we trying to predict?]
Model Output: [How will predictions be used?]
Success Metrics: [Specific measurable metrics]
## Constraints & Requirements
Performance: [Accuracy/speed requirements]
Scalability: [Expected volume and growth]
Regulatory: [Compliance requirements]
Interpretability: [Explainability needs]
Resources: [Budget, timeline, team constraints]
## Risk Assessment
Technical Risks: [Model performance, data quality, etc.]
Business Risks: [User adoption, regulatory, competitive, etc.]
Mitigation Strategies: [How to address identified risks]Common Problem Framing Pitfalls
Solution-First Thinking
Starting with "We need to use deep learning" instead of "We need to solve this business problem."
Ignoring Data Reality
Assuming you have the right data without validating quality, completeness, and relevance.
Unrealistic Expectations
Setting 99% accuracy goals when 85% would provide significant business value.
Ignoring Implementation
Not considering how the model will be deployed, maintained, and integrated with existing systems.
Next Steps
Once you've properly framed your ML problem, you're ready to move on to:
- • ML Systems Design: Architecture and infrastructure planning
- • Data Preparation: Collecting and preparing your datasets
- • Feature Engineering: Selecting and creating effective features
- • Model Selection: Choosing appropriate algorithms and approaches