How did our Logistic Regression model create the S-shaped curve we previously saw? The answer is the Sigmoid Function.

The Sigmoid Function is a special case of the more general Logistic Function, where Logistic Regression gets its name. Why is the Sigmoid Function so important? By plugging the log-odds into the Sigmoid Function, defined below, we map the log-odds z to the range [0,1].

  • e^(-z) is the exponential function, which can be written in numpy as np.exp(-z)

This enables our Logistic Regression model to output the probability of a sample belonging to the positive class, or in our case, a student passing the final exam!



Let’s create a Sigmoid Function of our own! Define a function called sigmoid() that takes z as a parameter. For now, have it return z.


Inside the function and above the return statement, create a variable denominator and set it equal to 1 plus the exponential of -z. Instead of returning z, return 1/denominator.


All done! Now test out your function by plugging in the calculated_log_odds we found in the previous exercise and saving the result to probabilities. Then, print probabilities.

Sign up to start coding

By signing up for Codecademy, you agree to Codecademy's Terms of Service & Privacy Policy.
Already have an account?