Q. In the context of linear regression, what does 'overfitting' mean?
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A.
The model performs well on training data but poorly on unseen data
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B.
The model is too simple to capture the underlying trend
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C.
The model has too few parameters
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D.
The model is perfectly accurate
Solution
Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on unseen data.
Correct Answer:
A
— The model performs well on training data but poorly on unseen data
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Q. What is the purpose of the training set in linear regression?
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A.
To evaluate the model's performance
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B.
To tune hyperparameters
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C.
To fit the model and learn the relationship between variables
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D.
To visualize the data
Solution
The training set is used to fit the model and learn the relationship between the independent and dependent variables.
Correct Answer:
C
— To fit the model and learn the relationship between variables
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Q. Which of the following is a potential problem when using linear regression?
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A.
Overfitting
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B.
Multicollinearity
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C.
Underfitting
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D.
All of the above
Solution
All of the listed options can be potential problems when using linear regression, affecting the model's performance and interpretability.
Correct Answer:
D
— All of the above
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Q. Which of the following techniques can help prevent overfitting in linear regression?
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A.
Increasing the number of features
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B.
Using regularization techniques like Lasso or Ridge
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C.
Decreasing the size of the training set
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D.
Ignoring outliers
Solution
Regularization techniques like Lasso or Ridge can help prevent overfitting by adding a penalty for larger coefficients.
Correct Answer:
B
— Using regularization techniques like Lasso or Ridge
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