In the last lesson, you learned that what you measure determines what the model can learn. But what happens when you measure the wrong things?
⚠️ The Hidden Truth
Sometimes the obvious measurements aren't the right ones. Features can correlate with an outcome without actually causing it.
🌧️ Weather Predictor
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Select
Collect
Train
Test
Investigate
📡 Choose Your Sensors
You're predicting tomorrow's weather. Pick 3 sensors you think will help predict rain.
Key Takeaways
- • Correlation ≠ Causation: Just because two things happen together doesn't mean one causes the other.
- • Missing the signal = blind model: If you don't measure the actual cause, no amount of data will help.
- • Obvious isn't always right: The features that seem most relevant might just be symptoms, not causes.
- • Domain knowledge matters: Understanding HOW things work helps you choose what to measure.