Classical models built from the math you already know
A model cannot improve until it can score its own mistakes. That scoring rule is called a loss function: plug in what the model predicted and what actually happened, and it hands back a single number for "how wrong". Training a model is nothing more than searching for the parameters that make this number as small as possible on average.
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▶ Choosing What "Wrong" Means: Loss Functions