Evaluation Metrics I

In this lesson, we cover two types of evaluation metrics: RMSE and the Precision, Recall, and Confusion Matrix.


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.

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