You need to use the precision as the evaluation metric. Which visualization should you use?

Last Updated on October 21, 2021 by Admin

You are evaluating a completed binary classification machine learning model.

You need to use the precision as the evaluation metric.

Which visualization should you use?

  • violin plot
  • Gradient descent
  • Scatter plot
  • Receiver Operating Characteristic (ROC) curve
Explanation:

Receiver operating characteristic (or ROC) is a plot of the correctly classified labels vs. the incorrectly classified labels for a particular model.

Incorrect Answers:
A: A violin plot is a visual that traditionally combines a box plot and a kernel density plot.

B: Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point.

C: A scatter plot graphs the actual values in your data against the values predicted by the model. The scatter plot displays the actual values along the X-axis, and displays the predicted values along the Y-axis. It also displays a line that illustrates the perfect prediction, where the predicted value exactly matches the actual value.

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