AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a linear regression model to predict house prices. The model's training error is very high, and its test error is nearly as high. Which term best describes this situation?
⚠ Common exam trap
It's easy for candidates to confuse 'high bias' with 'underfitting' as the best descriptor, but the question asks for the term that best describes the situation, and 'underfitting' is the direct behavioral term while 'high bias' is a contributing cause.
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
✓
Underfitting
Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in high training error and similarly high test error. In this linear regression scenario, the model fails to learn the relationship between features and house prices, leading to poor performance on both training and test sets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Underfitting
Why this is correct
The model is too simple to capture underlying patterns, resulting in high error on both training and test data. This occurs when assumptions are too rigid or features are insufficient, leading to poor generalization from the start. The term directly describes the observed condition of inadequate learning.
- ✗
Overfitting
Why it's wrong here
In contrast, overfitting shows low training error but high test error because the model memorizes noise and idiosyncrasies of the training set. The question specifies high error on both sets, which contradicts overfitting's hallmark of a gap between training and validation performance. Thus, overfitting is not the right term.
- ✗
High bias
Why it's wrong here
High bias is a root cause that often produces underfitting, but the question asks for the term that describes the model's behavior (high error on both sets). Bias refers to simplifying assumptions that lead to systematic errors, whereas underfitting is the observable symptom. Hence, while related, high bias is not the direct answer.
- ✗
High variance
Why it's wrong here
High variance is associated with overfitting, where the model is overly sensitive to training data fluctuations, leading to low training error and high test error. Since the scenario has high training error too, high variance does not match. Variance measures sensitivity to training set changes, not poor training performance.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Regression
Regression is a type of machine learning algorithm that predicts a continuous numeric output based on input data, used to model relationships between variables.
Key term
Underfitting
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both training and new data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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