What does increasing model flexibility typically lead to in regression analysis?

What does increasing model flexibility typically lead to in regression analysis?

• A. Improved prediction accuracy
• B. Reduced chances of overfitting
• C. Increased chances of overfitting
• D. Decreased bias in predictions

Answer: (C)

Increasing model flexibility in regression analysis allows a model to capture more complex relationships within the data. This often involves using more sophisticated statistical techniques or allowing for more parameters in the model. As the flexibility of a model increases, it can adapt better to the specific patterns present in the training data, which can often lead to capturing noise and subtle variations that do not generalize well to new, unseen data. As a result, while the model may show excellent performance on the training dataset, this enhancement in fitting can also cause it to perform poorly on validation or test datasets due to overfitting. Overfitting occurs when the model learns the random fluctuations in the training data as if they were significant underlying patterns. Thus, increased model flexibility tends to lead to greater chances of overfitting, as the model becomes too tailored to the training data rather than maintaining a balance that allows for generalization.

Increasing model flexibility in regression analysis allows a model to capture more complex relationships within the data. This often involves using more sophisticated statistical techniques or allowing for more parameters in the model. As the flexibility of a model increases, it can adapt better to the specific patterns present in the training data, which can often lead to capturing noise and subtle variations that do not generalize well to new, unseen data.

As a result, while the model may show excellent performance on the training dataset, this enhancement in fitting can also cause it to perform poorly on validation or test datasets due to overfitting. Overfitting occurs when the model learns the random fluctuations in the training data as if they were significant underlying patterns. Thus, increased model flexibility tends to lead to greater chances of overfitting, as the model becomes too tailored to the training data rather than maintaining a balance that allows for generalization.