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Question 22 - H13-311_V3.5 discussion

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Which of the following statements is false about the debugging and application of a regression model?

A.
If the model does not meet expectations, you need to use data cleansing and feature engineering.
Answers
A.
If the model does not meet expectations, you need to use data cleansing and feature engineering.
B.
After model training is complete, you need to use the test dataset to evaluate your model so that its generalization capability meets expectations.
Answers
B.
After model training is complete, you need to use the test dataset to evaluate your model so that its generalization capability meets expectations.
C.
If overfitting occurs, you can add a regularization term to the Lasso or ridge regression and adjust hyperparameters.
Answers
C.
If overfitting occurs, you can add a regularization term to the Lasso or ridge regression and adjust hyperparameters.
D.
If underfitting occurs, you can use a more complex regression model, for example, logistic regression.
Answers
D.
If underfitting occurs, you can use a more complex regression model, for example, logistic regression.
Suggested answer: D

Explanation:

Logistic regression is not a solution for underfitting in regression models, as it is used primarily for classification problems rather than regression tasks. If underfitting occurs, it means that the model is too simple to capture the underlying patterns in the data. Solutions include using a more complex regression model like polynomial regression or increasing the number of features in the dataset.

Other options like adding a regularization term for overfitting (Lasso or Ridge) and using data cleansing and feature engineering are correct methods for improving model performance.

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asked 26/09/2024
Marc Codó
45 questions
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