Decision Trees and Overfitting Quiz

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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?

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?

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?

With an attribute (feature) Signup and view all the answers

What does the Iterative Dichotomiser 3 (ID3) algorithm do to generate a decision tree?

Picks the best attribute to split at the root based on training data Signup and view all the answers

What does each leaf node in a decision tree represent?

A classification of positive and negative instances Signup and view all the answers

Why should a decision tree be pruned after construction?

To reduce overfitting on the training data Signup and view all the answers

What does each arc in a decision tree represent?

One possible value of the attribute at the node Signup and view all the answers

In machine learning, what impacts the ability to generalize as a function of training data and hypothesis space?

The simplicity of the hypothesis and the amount of training data Signup and view all the answers

Generalization in decision trees allows for what?

More than two classes to exist Signup and view all the answers

What is the purpose of post-pruning in decision tree construction?

To remove branches from leaf nodes to avoid overfitting Signup and view all the answers

In supervised learning, what is the purpose of training labeled examples?

To learn the unknown target function Signup and view all the answers

What is one way to avoid overfitting in decision trees?

Stop growing when data split not statistically significant Signup and view all the answers

Which action is included in the reduced error pruning process?

Greedily removing nodes to improve training set accuracy Signup and view all the answers

What indicates that a hypothesis overfits the training data?

The error rate on training data is higher than that of another hypothesis Signup and view all the answers

In decision tree pruning, what does it mean to remove the subtree rooted at a decision node?

Replacing the decision node with a leaf node Signup and view all the answers

What is a recommended step to reduce overfitting if noisy data is present?

Acquire more training data to dilute noise Signup and view all the answers

How does post-pruning in decision trees contribute to reducing overfitting?

By removing parts of the tree after it's grown fully Signup and view all the answers