Which of the following numbers is closest to the best parameter setting for the Decision Tree algorithm on the income data?

Question

BlackTom AI · Accepted Answer

To determine which number is closest to the best parameter setting for the Decision Tree on the income data, we need to think about how tree complexity affects performance. 
Option 75: Set to a relatively high value, this could allow the tree to become quite deep and fit noise in the training data, increasing overfitting. It is less likely to be near the optimal balance between bias and variance for typical income datasets. 
Option 146: This is even larger and would imply an even more complex model. Such a high setting tends to risk severe overfitting and may not generalize well, making it a poor candidate for the closest-to-optimal parameter. 
Option 33: A moderate value that reduces complexity compared to 75 or 146, potentially offering better generalization than very deep trees. However, it still may not hit the sweet spot that yields the best trade-off for the income data. 
Option 24: A smaller, more restrained setting that often aligns with common practical defaults aiming for a simpler model with better generalization on structured, tabular data like income datasets. It is plausible that 24 sits closest to the optimal setting, balancing the model’s capacity with the risk of overfitting. 
In summary, though exact tuning depends on the data split and evaluation, the larger numbers (75, 146) tend to push toward overfitting, the moderate 33 is plausible but not as close as 24, which is commonly associated with simpler, more generalizable trees.

2261 BUSQOM 0102 SEC1200 Project 2 Quiz

Which of the following numbers is closest to the best parameter setting for the Decision Tree algorithm on the income data?

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