More Loss Function¶
graph LR
%% Inputs
X1([x₁])
X2([x₂])
B([1])
%% Neuron and output
SUM([∑])
F([f])
Y([yᵢ])
%% Labeled edges only
X1 -- w₁ --> SUM
X2 -- w₂ --> SUM
B -- b --> SUM
SUM --> F --> Y
When to use & What
\[
\begin{array}{c|c|c|c}
\text{Loss function} & \text{Activation} & \text{Output} & \text{where to use} \\
\hline
\text{Hinge Loss} & \text{Step} & \text{Perceptron} & \text{Binary classification} \\
\hline
\text{Log-loss} \newline (\text{Binary cross-entropy}) & \text{Sigmoid} & \text{Logistic Regression} & \text{Binary class} \\
\hline
\text{Categorical} \newline \text{Cross entropy} & \text{Softmax} & \text{Softmax Regression} & \text{Multiclass class} \\
\hline
\text{MSE} & \text{linear} & \text{linear Regression} & \text{linear Regression} \\
\end{array}
\]