Podcast
Questions and Answers
What is the main purpose of the ID3 algorithm in the context of decision tree construction?
What is the main purpose of the ID3 algorithm in the context of decision tree construction?
- To reduce error during the testing phase
- To automatically find a good hypothesis for training data (correct)
- To quantify the ability to generalize in machine learning
- To generate a dataset for training purposes
What is the primary goal of using a decision tree in supervised learning?
What is the primary goal of using a decision tree in supervised learning?
- To classify examples as positive or negative instances (correct)
- To directly predict the outcome of new data points
- To represent the function to be learned with a tree structure
- To visualize the training data distribution
How does Occam’s razor relate to hypothesis selection in machine learning?
How does Occam’s razor relate to hypothesis selection in machine learning?
- It encourages using highly complex hypotheses for better accuracy
- It discourages considering the amount of training data
- It promotes using the simplest hypothesis consistent with data (correct)
- It suggests selecting hypotheses with many unneeded features
How are non-leaf nodes in a decision tree associated?
How are non-leaf nodes in a decision tree associated?
What does the Iterative Dichotomiser 3 (ID3) algorithm do to generate a decision tree?
What does the Iterative Dichotomiser 3 (ID3) algorithm do to generate a decision tree?
What does each leaf node in a decision tree represent?
What does each leaf node in a decision tree represent?
Why should a decision tree be pruned after construction?
Why should a decision tree be pruned after construction?
What does each arc in a decision tree represent?
What does each arc in a decision tree represent?
In machine learning, what impacts the ability to generalize as a function of training data and hypothesis space?
In machine learning, what impacts the ability to generalize as a function of training data and hypothesis space?
Generalization in decision trees allows for what?
Generalization in decision trees allows for what?
What is the purpose of post-pruning in decision tree construction?
What is the purpose of post-pruning in decision tree construction?
In supervised learning, what is the purpose of training labeled examples?
In supervised learning, what is the purpose of training labeled examples?
What is one way to avoid overfitting in decision trees?
What is one way to avoid overfitting in decision trees?
Which action is included in the reduced error pruning process?
Which action is included in the reduced error pruning process?
What indicates that a hypothesis overfits the training data?
What indicates that a hypothesis overfits the training data?
In decision tree pruning, what does it mean to remove the subtree rooted at a decision node?
In decision tree pruning, what does it mean to remove the subtree rooted at a decision node?
What is a recommended step to reduce overfitting if noisy data is present?
What is a recommended step to reduce overfitting if noisy data is present?
How does post-pruning in decision trees contribute to reducing overfitting?
How does post-pruning in decision trees contribute to reducing overfitting?
