Evaluation Metrics I
In this lesson, we cover two types of evaluation metrics: RMSE and the Precision, Recall, and Confusion Matrix.
We'll cover the following...
RMSE
We have learned about various ML models, but how do we evaluate them? Let’s understand the most important evaluation metrics.
For regression, we can use the difference between the actual and the predicted values.
Root mean square error (RMSE) is a typical performance measure for regression problems. It gives an idea of how much error the system typically makes in its predictions by measuring the differences between values predicted by the model and the actual values, e.g., actual prices vs predicted prices.
In linear regression, as shown in this figure, the observations (red) are assumed to be caused by random deviations (green) from an underlying relationship (blue line) between a dependent variable (y) and an independent variable (x). While finding the line of best fit, the goal is to minimize the distance shown in green. The predicted values (red points) should be as close as possible to the blue line. In the end, we select the blue line, which minimizes the sum of the overall error (the sum of green lines should be as close to zero as possible).
The equation ...