What’s stack generalization (stacking) in the world of machine learning?
A) A way to combine multiple models to boost prediction accuracy ??
B) A trick to shrink the feature space ?
C) An algorithm to group data points together ?
D) A method to deal with missing data pieces ?
Show answer
Answer: A) A method of combining multiple machine learning models to improve prediction accuracy.
Explanation: Stacking involves training multiple base models and combining their predictions using a meta-model to improve overall performance.
What’s true about the base models in stacking?
A) They must all be the same type, like a team of decision trees ?
B) They can be a mixed squad of different types, like decision trees and neural networks ?
C) They must train on different slices of the data ?
D) They don’t actually help with the final prediction ?
Show answer
Answer: B) They can be of different types (e.g., decision trees, neural networks, etc.).
Explanation: Stacking allows for a diverse set of base models, leveraging their different strengths to improve the ensemble’s performance.
In stacking, what’s the usual fate of the predictions from the base models?
A) They’re averaged to make the final call ?
B) They become the features for the meta-model ??
C) They’re tossed out, with only the meta-model’s prediction kept ??
D) They help figure out which features matter most ?
Show answer
Answer: B) They are used as features for the meta-model.
Explanation: The predictions from the base models are treated as new features that the meta-model uses to make the final prediction.
What’s a potential downside of using stacking in machine learning?
A) It can lead to overfitting if not properly managed ?
B) It’s only for linear models ?
C) It doesn’t play well with high-dimensional data ?
Show answer
Answer: A) It can lead to overfitting if not properly managed.
Explanation: If the base models or the meta-model are too complex or if there is not enough data, stacking can lead to overfitting, where the model performs well on training data but poorly on unseen data.
How can you help prevent overfitting in a stacking ensemble?
A) Use a simple meta-model ?
B) Incorporate different types of base models ?
C) Apply cross-validation during training ?
D) All of the above ??
Show answer
Answer: D) All of the above
Explanation: Various types of models, such as linear regression, support vector machines (SVM), and random forests, can be used as meta-models in stacking. The choice depends on the specific problem and the performance of the base models.
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