AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a regression model to predict energy consumption for a smart building. The model achieves very low error on the training data but performs significantly worse on a held-out validation set. Which technique would most directly address this problem?
⚠ Common exam trap
Watch out — candidates often confuse cross-validation (a performance evaluation method) with a technique to fix overfitting, or think hyperparameter tuning alone resolves overfitting without understanding that regularization is the specific mechanism to penalize complexity.
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
✓
Regularization
The model's low training error but high validation error indicates overfitting, where the model has memorized the training data rather than learning generalizable patterns. Regularization (e.g., L1 or L2) directly penalizes large coefficients, reducing model complexity and improving generalization 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.
- ✗
Feature engineering
Why it's wrong here
Feature engineering creates or transforms predictor variables to better capture underlying patterns in the data. While this can improve model accuracy, it does not constrain the model's hypothesis space or add a penalty for coefficient magnitude, so it does not directly counteract overfitting caused by excessively complex models. Overfitting from too many or overly flexible features would remain unless the feature set is aggressively simplified, which is not the primary mechanism of regularization.
- ✓
Regularization
Why this is correct
Regularization directly addresses overfitting by adding a penalty term to the loss function that grows with the magnitude of the model's coefficients. Techniques such as L1 (Lasso) and L2 (Ridge) force the model to keep weights small or drive some to zero, effectively reducing model complexity and variance. This penalty discourages the model from fitting noise in the training data, which is exactly why it is the correct method for the scenario described.
- ✗
Cross-validation
Why it's wrong here
Cross-validation is a resampling procedure used to estimate how well a model generalizes to unseen data by partitioning the dataset into training and validation folds. It does not change the model's learning algorithm or impose any constraint on the coefficients, so it cannot prevent overfitting itself. Rather, it helps detect overfitting by revealing a gap between training and validation performance, but the model complexity remains untouched and will still overfit if left unregularized.
- ✗
Hyperparameter tuning
Why it's wrong here
Hyperparameter tuning systematically searches for the best configuration of settings like learning rate, tree depth, or number of layers. While it can indirectly reduce overfitting if a less complex hyperparameter setting is chosen, it does not specifically penalize coefficient size or add a structural constraint to the objective function. The correct, direct remedy for overfitting in a regression model is regularization, which modifies the loss function itself rather than merely selecting among pre-defined model configurations.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Training data
Training data is a set of examples used to teach a machine learning model how to make predictions or decisions.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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