In a binary classification problem, the model predicts majority class for all inputs. What is this issue called?
Predicting majority class for all inputs indicates the model has high bias and is underfitting.
Why this answer
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.
Exam trap
Cisco 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.
How to eliminate wrong answers
Option B (Overfitting) is wrong because overfitting occurs when the model learns noise and details from the training data too well, resulting in high variance and poor generalization, not a constant prediction of the majority class. Option C (High variance) is wrong because high variance typically leads to models that are overly sensitive to small fluctuations in the training data, producing different predictions for similar inputs, not a uniform majority class output. Option D (Underfitting) is a related concept but is not the specific term for the issue described; underfitting refers to the model's inability to capture the training data's patterns, which can cause high bias, but the question asks for the name of the issue itself, which is high bias.