MLS-C01 Modeling Practice Question
Which TWO of the following are true about the bias-variance tradeoff?
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
✓
High variance can cause overfitting
The bias-variance tradeoff describes the balance between underfitting (high bias) and overfitting (high variance). Simple models have high bias and low variance, leading to underfitting. Complex models have low bias and high variance, leading to overfitting. Ensemble methods like bagging reduce variance by averaging multiple models. Therefore: - A is false: Bagging reduces variance, not increases. - B is false: Simple models have low variance, not high. - C is true: High variance causes the model to fit noise, i.e., overfitting. - D is true: High bias causes the model to miss relevant patterns, i.e., underfitting. - E is false: High variance models are typically too complex, not too simple.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ensemble methods like bagging increase variance
Why it's wrong here
Bagging reduces variance by averaging multiple models.
- ✗
Simple models tend to have high variance
Why it's wrong here
Simple models have high bias and low variance.
- ✓
High variance can cause overfitting
Why this is correct
High variance means the model is very sensitive to training data, leading to overfitting.
- ✓
High bias can cause underfitting
Why this is correct
High bias means the model is too simple to capture patterns, leading to underfitting.
- ✗
High variance models are typically too simple
Why it's wrong here
High variance models are complex and overfit the data.
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Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a linear regression model. After training, the model has a high bias and low variance. Which technique should the data scientist use to reduce bias?
easy- A.Decrease the model complexity
- ✓ B.Add more relevant features
- C.Apply L2 regularization (Ridge)
- D.Reduce the amount of training data
Why B: High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding more relevant features increases model complexity, allowing it to learn more from the data and reduce bias. This directly addresses the underfitting issue without increasing variance excessively, provided the features are meaningful.
Variation 2. A data scientist is using Amazon SageMaker built-in XGBoost algorithm to train a regression model. The training job completes successfully but the model performance on the test set is poor, with high bias. Which hyperparameter adjustment is most likely to help reduce bias?
medium- ✓ A.Increase the max_depth parameter.
- B.Reduce the num_round parameter.
- C.Increase the gamma parameter.
- D.Decrease the max_depth parameter.
Why A: High bias (underfitting) can be reduced by increasing the model complexity. Increasing max_depth allows more complex trees. Decreasing max_depth would increase bias. Increasing gamma increases regularization and bias. Reducing num_round (number of trees) reduces complexity.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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