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Errors in the Training Classifier

Explore how to calculate error in a neural network training classifier by comparing predicted and actual outputs. Understand why the separator line should be positioned to distinguish data points properly. This lesson helps you grasp error calculation fundamentals essential for training and optimizing neural network models in Python.

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Calculate an error

Let’s look at the first training example: the width is 3.03.0 and the length is 1.01.0 for a ladybug. If we tested the y=Axy = Ax function with this example, where xx is 3.03.0, we would get:

y=(0.25)×(3.0)=0.75y = (0.25) \times (3.0) = 0.75 ...