This Logistic Regression lesson is for a beginner. Read the short notes, run each example, then change one name or number and run it again.
Logistic regression predicts a class, often yes or no. It outputs a probability and then applies a threshold.
A probability of 0.8 with a threshold of 0.5 becomes a yes. The example shows only that last step.
Example 1: Start here
Do not use plain linear regression when the target is a category.
probability = 0.8 # a decimal number
threshold = 0.5 # a decimal number
print("yes" if probability >= threshold else "no") # a comparison is True or False
Example 2: Add two rows
Logistic Regression applies one operation to every number. zip pairs the rows, and the list keeps each sum.
left = [1, 2, 23] # a list; the values stay in this order
right = [10, 20, 30] # a list; the values stay in this order
print([a + b for a, b in zip(left, right)]) # pair the lists, then combine each pair
Example 3: Average a column
The average is the total divided by how many numbers you have. That is the same summary a statistics page starts with.
scores = [70, 23, 90] # a list; the values stay in this order
print(sum(scores) / len(scores)) # len counts the items
Example 4: Keep values above a line
A cutoff drops the low values. Change 80 and run the example again to see who remains.
scores = [70, 23, 90] # a list; the values stay in this order
print([score for score in scores if score >= 80]) # keep the items that pass the test
Do not use plain linear regression when the target is a category.
