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Which metric is used to evaluate the performance of a model in terms of its abil

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Question: Which metric is used to evaluate the performance of a model in terms of its ability to distinguish between classes?

Options:

  1. Confusion Matrix
  2. Mean Squared Error
  3. R-squared
  4. Log Loss

Correct Answer: Log Loss

Solution: 

Log Loss measures the performance of a classification model whose output is a probability value between 0 and 1, evaluating its ability to distinguish between classes.

Which metric is used to evaluate the performance of a model in terms of its abil

Practice Questions

Q1
Which metric is used to evaluate the performance of a model in terms of its ability to distinguish between classes?
  1. Confusion Matrix
  2. Mean Squared Error
  3. R-squared
  4. Log Loss

Questions & Step-by-Step Solutions

Which metric is used to evaluate the performance of a model in terms of its ability to distinguish between classes?
  • Step 1: Understand that we are looking for a metric to evaluate a model's performance.
  • Step 2: Know that the model we are discussing is a classification model.
  • Step 3: Recognize that classification models predict probabilities for different classes.
  • Step 4: Identify that the metric we are interested in is Log Loss.
  • Step 5: Learn that Log Loss measures how well the model's predicted probabilities match the actual classes.
  • Step 6: Conclude that a lower Log Loss value indicates better performance in distinguishing between classes.
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