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What does the term 'confusion matrix' refer to in classification tasks?

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Question: What does the term \'confusion matrix\' refer to in classification tasks?

Options:

  1. A matrix that shows the relationship between features
  2. A table used to evaluate the performance of a classification model
  3. A method for dimensionality reduction
  4. A technique for data normalization

Correct Answer: A table used to evaluate the performance of a classification model

Solution: 

A confusion matrix is a table that summarizes the performance of a classification model by showing true positives, false positives, true negatives, and false negatives.

What does the term 'confusion matrix' refer to in classification tasks?

Practice Questions

Q1
What does the term 'confusion matrix' refer to in classification tasks?
  1. A matrix that shows the relationship between features
  2. A table used to evaluate the performance of a classification model
  3. A method for dimensionality reduction
  4. A technique for data normalization

Questions & Step-by-Step Solutions

What does the term 'confusion matrix' refer to in classification tasks?
  • Step 1: Understand that a confusion matrix is a tool used to evaluate how well a classification model is performing.
  • Step 2: Know that it is presented in a table format.
  • Step 3: Identify the four key terms in the confusion matrix: true positives, false positives, true negatives, and false negatives.
  • Step 4: True positives (TP) are the cases where the model correctly predicts the positive class.
  • Step 5: False positives (FP) are the cases where the model incorrectly predicts the positive class when it is actually negative.
  • Step 6: True negatives (TN) are the cases where the model correctly predicts the negative class.
  • Step 7: False negatives (FN) are the cases where the model incorrectly predicts the negative class when it is actually positive.
  • Step 8: The confusion matrix helps visualize these outcomes and assess the model's accuracy.
  • Confusion Matrix – A table that summarizes the performance of a classification model by showing true positives, false positives, true negatives, and false negatives.
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