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Calculating RMSE instead of MSE as the final error

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thor-harsh 2023-08-17 13:49:31 +05:30 committed by GitHub
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machine_learning/decision_tree.py
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@ -164,7 +164,7 @@ def main():
to predict the label of 10 different test values. Here we should prefer
calculating Root Mean Squared Error over Mean Sqaured error because RMSE
should be used when you need to communicate your results in an understandable
way to end users or when penalising outliers is less of a priority.
way.
"""
x = np.arange(-1.0, 1.0, 0.005)
y = np.sin(x)