[02/Banas/13]

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Questions and Answers

Which type of data mining uses machine learning algorithms to build predictive models without providing any explanation of how the models work?

  • Black box data mining (correct)
  • Transparent data mining
  • Interpretable data mining
  • White box data mining

Which type of data mining is more focused on accuracy rather than interpretability?

  • Interpretable data mining
  • White box data mining
  • Transparent data mining
  • Black box data mining (correct)

What is one of the main benefits of black box data mining?

  • It can be used to build predictive models that are more accurate than white box models (correct)
  • It is limited by the constraints of human interpretability
  • It provides detailed explanations of how the models work
  • It is easy to understand and interpret

Which type of data mining algorithm is often used in applications where accuracy is more important than interpretability?

<p>Black box data mining (C)</p> Signup and view all the answers

What is one of the main drawbacks of black box data mining?

<p>Difficult to understand and interpret (C)</p> Signup and view all the answers

Which type of data mining algorithm uses algorithms that are more transparent and can be explained in human terms?

<p>White box data mining (B)</p> Signup and view all the answers

What is one of the main benefits of black box data mining?

<p>Building more accurate predictive models (B)</p> Signup and view all the answers

Which type of data mining is focused on interpretability rather than accuracy?

<p>White box data mining (D)</p> Signup and view all the answers

Which type of data mining algorithm is difficult to trust and can make it difficult to identify potential biases in the data?

<p>Black box data mining (D)</p> Signup and view all the answers

Which type of data mining algorithm is not limited by the constraints of human interpretability?

<p>Black box data mining (D)</p> Signup and view all the answers

Which type of data mining algorithm is often used in fraud detection and spam filtering?

<p>Black box data mining (D)</p> Signup and view all the answers

Which type of data mining algorithm can be explained in human terms?

<p>White box data mining (B)</p> Signup and view all the answers

Black box data mining uses machine learning algorithms to build predictive models without providing any explanation of how the models work.

<p>True (A)</p> Signup and view all the answers

White box data mining algorithms are more accurate than black box algorithms.

<p>False (B)</p> Signup and view all the answers

Black box data mining is often used in applications where interpretability is more important than accuracy.

<p>False (B)</p> Signup and view all the answers

Black box data mining can be used to build predictive models that are more accurate than white box models.

<p>True (A)</p> Signup and view all the answers

The main drawback of black box data mining is that the models can be difficult to understand and interpret.

<p>True (A)</p> Signup and view all the answers

Random forests is an example of a black box data mining algorithm.

<p>True (A)</p> Signup and view all the answers

Black box data mining algorithms are limited by the constraints of human interpretability.

<p>False (B)</p> Signup and view all the answers

Black box data mining is not used in fraud detection and spam filtering.

<p>False (B)</p> Signup and view all the answers

One of the main benefits of black box data mining is its ability to identify potential biases in the data.

<p>False (B)</p> Signup and view all the answers

Match the following terms with their correct descriptions:

<p>Black box data mining = Type of data mining that uses machine learning algorithms to build predictive models without providing any explanation of how the models work White box data mining = Type of data mining that uses algorithms that are more transparent and can be explained in human terms Neural networks = Example of a black box data mining algorithm Interpretability = Concept that is more important in white box data mining than in black box data mining</p> Signup and view all the answers

Match the following data mining algorithms with their correct types:

<p>Neural networks = Black box data mining algorithm Support vector machines = Black box data mining algorithm Decision trees = Black box data mining algorithm Random forests = Black box data mining algorithm</p> Signup and view all the answers

Match the following statements with the correct type of data mining:

<p>Uses algorithms that are more transparent and can be explained in human terms = White box data mining Often used in applications where accuracy is more important than interpretability = Black box data mining Can be used to build predictive models that are more accurate than white box models = Black box data mining Models can be difficult to understand and interpret = Black box data mining</p> Signup and view all the answers

Match the following terms with their correct descriptions:

<p>Black box data mining = Often used in fraud detection and spam filtering White box data mining = Focused on interpretability rather than accuracy Neural networks = Example of a black box data mining algorithm Accuracy = Concept that is more important in black box data mining than in white box data mining</p> Signup and view all the answers

Match the following data mining algorithms with their correct types:

<p>Neural networks = Black box data mining algorithm Support vector machines = Black box data mining algorithm Decision trees = Black box data mining algorithm Random forests = Black box data mining algorithm</p> Signup and view all the answers

Match the following statements with the correct type of data mining:

<p>Uses machine learning algorithms to build predictive models without providing any explanation of how the models work = Black box data mining Often used in applications where accuracy is more important than interpretability = Black box data mining Can be used to build predictive models that are more accurate than white box models = Black box data mining Models can be difficult to understand and interpret = Black box data mining</p> Signup and view all the answers

Match the following terms with their correct descriptions:

<p>Black box data mining = Often used in fraud detection and spam filtering White box data mining = Focused on interpretability rather than accuracy Neural networks = Example of a black box data mining algorithm Accuracy = Concept that is more important in black box data mining than in white box data mining</p> Signup and view all the answers

Match the following data mining algorithms with their correct types:

<p>Neural networks = Black box data mining algorithm Support vector machines = Black box data mining algorithm Decision trees = Black box data mining algorithm Random forests = Black box data mining algorithm</p> Signup and view all the answers

Match the following statements with the correct type of data mining:

<p>Uses machine learning algorithms to build predictive models without providing any explanation of how the models work = Black box data mining Often used in applications where accuracy is more important than interpretability = Black box data mining Can be used to build predictive models that are more accurate than white box models = Black box data mining Models can be difficult to understand and interpret = Black box data mining</p> Signup and view all the answers

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