AIF-C01 Fundamentals of AI and ML Practice Question
In a binary classification problem, the model predicts majority class for all inputs. What is this issue called?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between 'high bias' and 'underfitting' as separate concepts, where underfitting is the symptom and high bias is the cause, so candidates may incorrectly select underfitting when the question explicitly asks for the name of the issue.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
High bias
When a model predicts the majority class for all inputs, it indicates that the model is too simplistic and fails to capture the underlying patterns in the data. This is a classic symptom of high bias, where the model makes strong assumptions about the data distribution, leading to systematic underperformance on the minority class. In machine learning, high bias often results from an overly simple algorithm or insufficient model capacity, causing the model to underfit the training data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
High bias
Why this is correct
Predicting majority class for all inputs indicates the model has high bias and is underfitting.
- ✗
Overfitting
Why it's wrong here
Overfitting means model performs well on training but poorly on test due to noise.
- ✗
High variance
Why it's wrong here
High variance means model is sensitive to small fluctuations, not predicting constant output.
- ✗
Underfitting
Why it's wrong here
Underfitting means model is too simple to capture patterns, but predicting majority class is a specific symptom of high bias.
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