AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a regression model to predict housing prices. The model uses polynomial features up to degree 5. It achieves an R-squared of 0.95 on the training set but only 0.60 on the test set. Which problem is the model most likely experiencing?
⚠ Common exam trap
A common mix-up: candidates confuse overfitting with underfitting, but the key indicator is the large gap between high training performance and low test performance, not uniformly low performance.
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
✓
Overfitting
The model performs exceptionally well on the training data (R-squared 0.95) but poorly on the test data (R-squared 0.60), which is the classic symptom of overfitting. Using polynomial features up to degree 5 introduces high model complexity, causing the model to learn noise and specific patterns in the training set that do not generalize to unseen 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.
- ✗
Underfitting
Why it's wrong here
Underfitting reflects a model with excessive bias, such as a linear regression applied to inherently nonlinear data. It would produce high error on both the training and test sets, because the model fails to capture the underlying relationship even in the data it has seen. In contrast, the scenario describes strong training performance with weak test performance, which is the opposite of underfitting.
- ✓
Overfitting
Why this is correct
Overfitting occurs when a complex model, such as a high-degree polynomial or a deep regression tree, memorizes the noise and idiosyncrasies of the training data. This yields near-perfect training predictions, but the model does not generalize to new data, producing a large train–test performance gap. The hallmark is high variance and low bias on training, with poor performance on validation or test sets.
- ✗
Data leakage
Why it's wrong here
Data leakage arises when information from the test set inadvertently influences training, such as scaling features using statistics computed from the full dataset before a split. If leakage were present, we would typically see artificially high performance on the test set as well, because the model already 'knows' the answer. The observed low test performance therefore contradicts the typical leakage signature.
- ✗
Multicollinearity
Why it's wrong here
Multicollinearity refers to high correlations among predictor variables, which inflates the variance of coefficient estimates and makes individual effects difficult to isolate. While it can make models unstable, it does not inherently create a systematic large gap between training and test performance unless combined with overfitting or extrapolation. The reported train–test discrepancy is best explained by the model fitting noise, not by collinearity among features.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Overfitting
Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, causing it to perform poorly on new, unseen data.
Key term
Test data
Test data is a set of information used to verify that a software application or system works correctly under various conditions.
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