Sigmoid Function
The Sigmoid function, also known as the Logistic Function, squeezes numbers into a probabilitylike range between 0 and 1.^{1} Used in Binary Classification model architectures to compute loss on discrete labels, that is, labels that are either 1 or 0 (hotdog or not hotdog). The equation is:
$S(x) = \frac{1}{1 + e^{x}}$
Intuitively, when x
is infinity ($e^{\infty}=0$), the Sigmoid becomes $\frac{1}{1}$ and when x
is infinity ($e^{\infty} = \infty$) the Sigmoid becomes $\frac{1}{inf}$. That means the model is incentivised to output values as high as possible in a positive case, and low for the negative case.
It is named Sigmoid because of its Slike Function Shape. Its name combines the lowercase sigma character and the suffix oid, which means similar to.
It can be described and plotted in Python, as follows:
import math, matplotlib.pyplot as plt
def sigmoid_function(x):
return 1/(1+math.e**(x))
inputs = list(range(10, 10, 1))
labels = [sigmoid_function(i) for i in inputs]
fig,ax = plt.subplots(figsize=(6,4))
ax.plot(inputs,labels)
plt.show()

Technically, there are many Sigmoid functions, each that return different ranges of numbers. This function's correct name is the Logistic Function. An alternative function that outputs values between 1 and 1 is called the Hyperbolic Tangent. However, in Machine Learning, sigmoid always refers to the Logistic Function. ↩
References
Emily Fox and Carlo Guestrin. Machine Learning: Classification. 2016. URL: https://www.coursera.org/learn/mlclassification?specialization=machinelearning. ↩