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A classifier can be doing well when it has low bias and low variance.

Low Bias and Low Variance Bias-Variance Example

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Characteristics of a well-performing classifier

Classifier performance criteria

Achieving great performance requires _____ variance

Matching performance terms to a successful classifier

Reasoning about great classifier performance

Question:
Describe the ideal state of a classifier in terms of the two major sources of error, and explain what achieving this state indicates about its performance.

Sample answer:
The ideal state of a classifier is to have both low bias and low variance. Since bias and variance are the two major sources of error, minimizing both means the classifier is making very few errors on the training set and generalizes well to the dev/test sets. Achieving this state indicates that the classifier is doing well and has reached great performance.

Key points:
- The classifier must have low bias.
- The classifier must have low variance.
- Achieving both indicates the classifier is doing well.
- This state represents great performance.

Rubric:
A strong answer will explicitly mention low bias and low variance and connect these to the concept of the classifier doing well or achieving great performance.

Analyzing a well-performing classifier

Case context:
A machine learning team presents their latest model iteration. They demonstrate that the classifier has very low error on the training set and similarly low error on the dev set, indicating low bias and low variance.

Question:
Based on their presentation, how should you assess the classifier's performance, and what feedback should you give the team?

Sample answer:
Because the classifier has achieved both low bias and low variance, you should assess that the classifier is doing well. The appropriate feedback is to congratulate the team on achieving this great performance.

Key points:
- Assess the classifier as doing well.
- The success is due to having low bias and low variance.
- Congratulate the team on achieving great performance.

Rubric:
The response should accurately diagnose the model as doing well based on the low bias and low variance, and suggest positive feedback or congratulations.

Evaluating a team's classifier results

Question:
What two conditions must a classifier meet regarding its major sources of error to be considered as doing well?

Sample answer:
A classifier must have both low bias and low variance to be considered doing well.

Key points:
- Low bias
- Low variance

Rubric:
The answer must identify low bias and low variance.

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