Logistic Regression: Sigmoid Function Python Code

Logistic Regression - Sigmoid Function Plot

In this post, you will learn about the following:

  • How to represent the probability that an event will take place with the asssociated features (attributes / independent features)
  • Sigmoid function python code

Probability as Sigmoid Function

The below is the Logit Function code representing association between the probability that an event will occur and independent features.

$$Logit Function = \log(\frac{P}{(1-P)}) = {w_0} + {w_1}{x_1} + {w_2}{x_2} + …. + {w_n}{x_n}$$

$$Logit Function = \log(\frac{P}{(1-P)}) = W^TX$$

$$P = \frac{1}{1 + e^-W^TX}$$

The above equation can be called as sigmoid function.

Python Code for Sigmoid Function

import numpy as np
import matplotlib.pyplot as plt

# Sigmoid function
def sigmoid(z):
    return 1 / (1 + np.exp(-z))
# Creating sample Z points
z = np.arange(-5, 5, 0.1)

# Invoking Sigmoid function on all Z points
phi_z = sigmoid(z)

# Plotting the Sigmoid function
plt.plot(z, phi_z)
plt.axvline(0.0, color='k')
plt.yticks([0.0, 0.5, 1.0])
ax = plt.gca()

Executing the above code would result in the following plot:

Logistic Regression - Sigmoid Function Plot

Fig 1: Logistic Regression – Sigmoid Function Plot

Pay attention to some of the following in above plot:

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I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
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