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Machine Learning: Difference between revisions
→Loss functions
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* If our model is <math>g(\theta^Tx^{(i)})</math> where <math>g(x)</math> is the sigmoid function <math>\frac{e^x}{1+e^x}</math> then this is convex | * If our model is <math>g(\theta^Tx^{(i)})</math> where <math>g(x)</math> is the sigmoid function <math>\frac{e^x}{1+e^x}</math> then this is convex | ||
{{hidden | Proof | | <!-- {{hidden | Proof | | ||
<math> | <math> | ||
\begin{aligned} | \begin{aligned} | ||
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</math><br> | </math><br> | ||
which is a PSD matrix | which is a PSD matrix | ||
}} | }} --> | ||
===Hinge Loss=== | ===Hinge Loss=== | ||