Bias and variance on the AI Learning track. Bias is error from a too-simple story. Variance is error from chasing noise, so a new sample would give a different model. You trade them. A very flexible model has low bias and high variance.
This lesson assumes you already worked through Underfitting.
The idea in practice
If validation swings wildly when you change a few rows, variance is high. If every model misses the same cases, bias or missing features is more likely.
A concrete check
goal = {
'track': 'AI Learning',
'lesson': 'Bias and variance',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
Using the words as insults instead of as a diagnosis of what to change next. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
For a small dataset, say whether you would reduce flexibility or add features, and why.
Self-check
- Say Bias and variance in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.