Which of the following is a key advantage of decision trees in machine learning, as described in the lecture?

Question

BlackTom AI · Accepted Answer

When evaluating the question about a key advantage of decision trees in machine learning, it helps to examine what each option asserts and compare it to the typical characteristics of decision trees.
Option 1: 'Decision trees automatically require less data preprocessing since they convert all features into binary inputs before modeling.' This is misleading. While decision trees can handle different types of features, they do not inherently require converting all features into binary inputs, and this claim understates common preprocessing needs for other feature types. It also exaggerates a preprocessing advantage that is not central to decision trees.
Option 2: 'Decision trees provide clear interpretability because the series of nested decision rules can be easily understood by humans.' This captures a widely recognized strength of decision trees: their rule-based structure is transparent, and you can trace a prediction by following the path through the tree. This interpretability is often cited as a practical advantage for understanding model decisions and communicating them to stakeholders.
Option 3: 'Decision trees are inherently resistant to overfitting and always generalize well to new data.' This is incorrect. In fact, decision trees can overfit the training data, especially when grown deep without pruning or regularization. Saying they always generalize well contradicts observed behavior and common modeling experience.
Option 4: 'Decision trees eliminate the need for a training/testing split since their rules are universally applicable to all data points.' This is false. Like any model, decision trees require evaluation on unseen data to estimate generalization performance, and using a training/testing split (or cross-validation) is standard practice to assess how well the tree will perform on new data.
Option 5: 'Decision trees are preferred because they consistently achieve higher prediction accuracy than methods like support vector machines and neural networks.' This claim is not generally true. While trees have advantages, their accuracy relative to SVMs or neural networks depends on the dataset and problem; trees do not universally outperform these methods. In some cases, other models can achieve equal or better accuracy.
In summary, Option 2 stands out as the true advantage by highlighting the interpretable, rule-based structure of decision trees, whereas the other options either misstate preprocessing behavior, overstate robustness to overfitting, misrepresent the need for evaluation, or oversell comparative accuracy.

2254 BIOSC 1544 SEC1000 Quiz #6: Machine Learning

Which of the following is a key advantage of decision trees in machine learning, as described in the lecture?

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