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.