Q. What does overfitting refer to in machine learning?
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A.
A model that performs well on training data but poorly on unseen data
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B.
A model that generalizes well to new data
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C.
A model that is too simple for the data
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D.
A model that has too few features
Solution
Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize.
Correct Answer:
A
— A model that performs well on training data but poorly on unseen data
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Q. What does PCA stand for in the context of feature engineering?
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A.
Partial Component Analysis
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B.
Principal Component Analysis
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C.
Predictive Component Analysis
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D.
Probabilistic Component Analysis
Solution
PCA stands for Principal Component Analysis, a technique used for dimensionality reduction.
Correct Answer:
B
— Principal Component Analysis
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Q. What is the purpose of using a validation set?
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A.
To train the model
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B.
To test the model's performance
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C.
To tune hyperparameters
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D.
To visualize the data
Solution
A validation set is used to tune hyperparameters and select the best model configuration.
Correct Answer:
C
— To tune hyperparameters
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Q. Which technique can help prevent overfitting?
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A.
Increasing the number of features
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B.
Using a more complex model
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C.
Cross-validation
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D.
Ignoring validation data
Solution
Cross-validation helps assess model performance on unseen data, reducing the risk of overfitting.
Correct Answer:
C
— Cross-validation
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