MLS-C01 Modeling Practice Question
A data scientist is training a multiclass classification model to categorize support tickets into 50 categories. The dataset has 100,000 labeled tickets. The scientist uses a random forest classifier with 100 trees. The model achieves 90% accuracy on the test set, but the F1-score for some rare categories is below 0.1. The scientist wants to improve performance on rare categories without significantly reducing overall accuracy. Which approach should the scientist try?
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
✓
Use class_weight='balanced' or compute custom class weights
(use class_weight='balanced' or compute custom class weights) helps the model focus on rare classes by assigning higher penalties to misclassifications of minority classes. Option B (reduce the number of trees to 50) may reduce model capacity and hurt overall performance. Option C (switch to a one-vs-rest logistic regression model) may not handle rare classes well without class weighting. Option A (increase the maximum depth of trees) could lead to overfitting and may not address class imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the maximum depth of trees
Why it's wrong here
Deeper trees may overfit to majority classes.
- ✗
Reduce the number of trees to 50 to prevent overfitting
Why it's wrong here
Fewer trees may reduce performance on all classes.
- ✗
Switch to a one-vs-rest logistic regression model
Why it's wrong here
Logistic regression may underfit complex patterns.
- ✓
Use class_weight='balanced' or compute custom class weights
Why this is correct
Class weights penalize misclassifications of rare classes more heavily.
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