MLS-C01 Modeling Practice Question
A company is building a sentiment analysis model for customer reviews. The dataset includes 10,000 positive and 10,000 negative reviews. The data scientist splits the data into 70% training, 15% validation, and 15% test sets. After training, the model achieves 99% accuracy on training set but only 82% on validation set. What is the most likely 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
✓
The model is overfitting
The 99% training accuracy versus 82% validation accuracy indicates the model has memorized the training data but fails to generalize to unseen data, which is classic overfitting. Option D is correct. Option A is incorrect because data leakage would typically cause both training and validation accuracy to be high and similar. Option B is incorrect because the dataset is balanced (10,000 positive and 10,000 negative). Option C is incorrect because underfitting would show low accuracy on both training and validation sets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
There is data leakage from validation to training
Why it's wrong here
Data leakage would likely cause high validation accuracy, not a gap.
- ✗
The dataset is imbalanced
Why it's wrong here
The dataset has equal positive and negative examples, so it is balanced.
- ✗
The model is underfitting
Why it's wrong here
Underfitting would show low accuracy on both training and validation sets.
- ✓
The model is overfitting
Why this is correct
High training accuracy with significantly lower validation accuracy is a classic sign of overfitting.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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