MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset for a binary classification problem. The dataset has 10,000 samples and 200 features. After splitting into training (80%) and test (20%), the data scientist trains a decision tree classifier and achieves 100% accuracy on the training set but only 55% on the test set. Which step should the data scientist take first to address this issue?
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
✓
Prune the decision tree to reduce complexity
The large discrepancy between training and test accuracy indicates overfitting, and pruning the decision tree (e.g., limiting max_depth) reduces overfitting. Option A is wrong because cross-validation is a technique to evaluate model performance but does not directly fix overfitting. Option B is wrong because more data may help but is not the first step; also data is limited. Option C is wrong because more features may worsen overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use cross-validation to evaluate model performance
Why it's wrong here
Cross-validation is a technique to evaluate model performance but does not directly fix overfitting.
- ✗
Collect more training data
Why it's wrong here
Why B is wrong
- ✗
Add more features to the model
Why it's wrong here
Adding more features may worsen overfitting.
- ✓
Prune the decision tree to reduce complexity
Why this is correct
Why D is correct
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