Lasso Regression Post Quiz

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Question 1 of 16

1. Question
What unique feature does Lasso Regression offer compared to Linear and Ridge Regression?
- It can predict categorical outcomes.
- It introduces a penalty term based on the absolute value of the coefficients.
- It can automatically select important features by shrinking some coefficients to zero.
- It requires no feature scaling.
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Question 2 of 16

2. Question
What does Lasso stand for in Lasso Regression?
- Large Algorithm for Subset Selection Optimization
- Least Absolute Shrinkage and Selection Operator
- Linear Algorithm for Statistical Solution Optimization
- Leading Analytics for Simplified Solutions Online
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Question 3 of 16

3. Question
What is the primary advantage of Lasso Regression?
- Increased computational speed
- Better performance on linear datasets
- Ability to handle overfitting and perform feature selection
- It works without any data pre-processing
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Question 4 of 16

4. Question
In Lasso Regression, what effect does the penalty term have on the coefficients of predictors?
- Increases their value proportionally
- Has no effect on the coefficients
- Can reduce some coefficients to exactly zero
- Makes all coefficients equal
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Question 5 of 16

5. Question
Which scenario best demonstrates the feature selection capability of Lasso Regression?
- A model where all features have equal coefficients
- A model that uses all available features without any preference
- A model that identifies and uses only the most relevant features
- A model that exclusively focuses on minimizing the penalty term
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Question 6 of 16

6. Question
How does Lasso Regression differ from Ridge Regression in terms of feature selection?
- Lasso can shrink coefficients to zero, effectively selecting features.
- Ridge selects more features than Lasso for model construction.
- Lasso and Ridge use the same method for feature selection.
- Ridge can also shrink coefficients to zero.
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Question 7 of 16

7. Question
What is a key consideration when using Lasso Regression?
- Ensuring that the dataset is purely categorical
- Choosing the appropriate value for the penalty term λ
- Applying it only to small datasets
- Avoiding any data scaling before model training
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Question 8 of 16

8. Question
Which of the following is a limitation of Lasso Regression?
- It cannot be used with linear models.
- It may exclude important predictors if they are correlated with others.
- It only works with numerical data.
- It always outperforms other regression models in prediction accuracy.
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Question 9 of 16

9. Question
In the context of the Wine Quality dataset, how does Lasso Regression help in making predictions?
- By using all available physicochemical properties without prioritization
- By predicting wine quality based solely on the alcohol content
- By selecting the most relevant physicochemical properties for predicting wine quality
- By focusing exclusively on the properties with the highest measurements
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Question 10 of 16

10. Question
What is the key difference in the penalty term between Lasso and Ridge Regression?
- Lasso uses the squared value of coefficients for the penalty.
- Ridge does not use any penalty term.
- Lasso uses the absolute value of coefficients for the penalty.
- Ridge penalizes the number of features used.
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Question 11 of 16

11. Question
Why is Lasso Regression particularly useful in models with many features?
- It increases the model's complexity.
- It selects the most relevant features, reducing model complexity.
- It makes calculation of coefficients faster.
- It enhances the prediction accuracy by using all features.
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Question 12 of 16

12. Question
In Lasso Regression, what happens as the value of λ (lambda) increases?
- More features are selected for the model.
- The model becomes more complex.
- The penalty on the coefficients decreases.
- Coefficients of less important features are more likely to be shrunk to zero.
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Question 13 of 16

13. Question
Which outcome is a direct consequence of Lasso Regression’s feature selection capability?
- Increased prediction accuracy on training data
- Simplification of the model for easier interpretation
- Automatic conversion of categorical variables to numeric
- Elimination of the need for data scaling
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Question 14 of 16

14. Question
What challenges can arise when using Lasso Regression for datasets where features are highly correlated?
- It will select all correlated features.
- It may discard some important features.
- It will increase the coefficients of correlated features.
- Correlation between features has no impact on Lasso Regression.
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Question 15 of 16

15. Question
How does the Lasso Regression penalty term impact model complexity?
- It increases complexity by adding more features.
- It has no impact on complexity.
- It reduces complexity by possibly eliminating features.
- It switches the model to a non-linear approach.
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Question 16 of 16

16. Question
Which scenario exemplifies the best use of Lasso Regression?
- When the dataset has a small number of highly important features.
- When every feature in the dataset is equally important.
- When dealing with a dataset that has a large number of potentially irrelevant features.
- When features in the dataset are not correlated at all.
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