Question

Which of the following statements is correct about overfitting?

a. Tackling an overfitting problem can help improve a model's stability
b. Overfitting can only affect linear and logistic models, not other types of models
c. High R-squared always indicates an overfitting problem
d. To address overfitting, we need to add more predictor variables to our model

Answer 1

Tackling an overfitting problem can help improve a model's stability. Overfitting can affect any type of model. High R-squared does not always indicate an overfitting problem. To address overfitting, we often need to reduce the number of predictor variables in our model.

Step-by-step explanation:

Option A is correct. Tackling an overfitting problem can help improve a model's stability. Overfitting occurs when a model becomes too complex and tries to fit the noise in the data rather than the underlying pattern. By reducing overfitting, we can create a more stable and reliable model.

Option B is incorrect. Overfitting can affect any type of model, not just linear and logistic models. It is a common problem in machine learning and statistical modeling.

Option C is incorrect. High R-squared does not always indicate an overfitting problem. R-squared measures the proportion of the response variable's variance that can be explained by the predictor variables. It is possible to have a high R-squared without overfitting if the model is accurately capturing the underlying relationship in the data.

Option D is incorrect. To address overfitting, we often need to reduce the number of predictor variables in our model instead of adding more. Adding more predictor variables can actually exacerbate the overfitting problem.