AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a multiclass classification model to identify different species of flowers (Iris setosa, Iris virginica, Iris versicolor). The overall accuracy is 94%, but the accuracy for the Iris virginica class is only 60%. Which additional metric should the data scientist examine to better understand the model's performance on the minority class?
⚠ Common exam trap
Watch out — candidates often choose precision or recall individually, not realizing that the F1-score is specifically designed to combine both metrics and is the standard choice for evaluating performance on imbalanced classes in classification tasks.
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
✓
F1-score
The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives. Since the model has high overall accuracy but poor performance on the minority class (Iris virginica), the F1-score is ideal for evaluating the model's effectiveness on that class, as it accounts for class imbalance better than accuracy alone.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Precision
Why it's wrong here
Precision measures how many of the samples predicted as Iris virginica actually are virginica, computed as TP / (TP + FP). A high precision means few false positives, but it entirely ignores false negatives—so the model could miss half of the true virginica samples and still achieve high precision. Because the goal is to understand why the virginica class underperforms, precision alone hides recall issues and gives an incomplete diagnostic picture; F1-score combines both perspectives.
- ✗
Recall
Why it's wrong here
Recall measures how many of the true Iris virginica samples were correctly identified, computed as TP / (TP + FN). High recall minimizes false negatives but says nothing about false positives, so a model that labels nearly every flower as virginica would have perfect recall while being practically useless. Since the underperforming class likely suffers from a precision-recall tradeoff, recall alone cannot reveal the full problem; F1-score balances both so that neither false positives nor false negatives dominate.
- ✓
F1-score
Why this is correct
F1-score is the harmonic mean of precision and recall, calculated as 2 × (precision × recall) / (precision + recall). It is especially useful when one class—here Iris virginica—is performing poorly, because the harmonic mean imposes a severe penalty if either precision or recall is low, forcing the model to do well on both. Unlike overall accuracy, which can be dominated by the other two well-classified species, per-class F1 directly measures the underperforming class and is the correct metric for diagnosing and tracking its performance.
- ✗
Mean Absolute Error (MAE)
Why it's wrong here
Mean Absolute Error (MAE) is a regression metric defined as the average absolute difference between predicted and actual continuous values, MAE = (1/n) Σ |actual − predicted|. It assumes the target variable is numeric and that differences between values are meaningful, such as temperature or price. Flower species are discrete categorical labels with no ordinal relationship, so computing absolute differences between class labels is undefined and MAE cannot be applied. Therefore, it is irrelevant to evaluating a multiclass classification model, where metrics like F1-score, precision, and recall are used.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
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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