Recognize Sampling Bias
Training mostly contains downtown routes while deployment includes rural trips.
Recognize Sampling Bias explained visually
Training mostly contains downtown routes while deployment includes rural trips. English captions are optional and start off. The complete transcript follows the written lesson.
What you will learn
Training mostly contains downtown routes while deployment includes rural trips.
A practical question
Most training deliveries came from downtown. The dispatcher now wants predictions for rural routes. A strong score on downtown records does not guarantee the same result where travel times and weather effects differ.
The useful idea
Compare the training sample with the incoming trips by route, distance, time, and other known conditions. Identify where coverage is thin. You can sometimes weight observed cases to better match deployment, but a weight cannot invent outcomes for routes never seen.
Work through the example
Collect a labeled rural sample and evaluate it separately. If the model fails, inspect whether features or labels mean the same thing in both settings before adjusting weights or training a larger model. Keep the target population explicit in every performance claim.
Limits and next step
Next, we'll focus on outcomes that are rare even in a representative sample: severe delays.
Worked example
Compare route and distance distributions between training records and new requests.
Quick check: test your understanding
Try it yourself: In the situation described above, explain the method and one reason its output might be misleading.
Check the reasoning
Sampling bias means observed examples do not represent the target population. A large dataset can still give unreliable predictions for underrepresented routes.
What is the main idea behind recognize sampling bias?
Sampling bias means observed examples do not represent the target population.
How is it used in the example?
Compare route and distance distributions between training records and new requests.
What limitation should be checked?
A large dataset can still give unreliable predictions for underrepresented routes.
Further reading
Machine Learning — Tom M. Mitchell.
Original ToolsInVision teaching examples. Editorial approach · Suggest a correction
Accessible referenceLesson 22 transcript131 words · Video transcript · Matches the published narration and captionsOpen transcript
Most training deliveries came from downtown. The dispatcher now wants predictions for rural routes. A strong score on downtown records does not guarantee the same result where travel times and weather effects differ.
Compare the training sample with the incoming trips by route, distance, time, and other known conditions. Identify where coverage is thin. You can sometimes weight observed cases to better match deployment, but a weight cannot invent outcomes for routes never seen.
Collect a labeled rural sample and evaluate it separately. If the model fails, inspect whether features or labels mean the same thing in both settings before adjusting weights or training a larger model. Keep the target population explicit in every performance claim.
Next, we'll focus on outcomes that are rare even in a representative sample: severe delays.