This Scale lesson is for a beginner. Read the short notes, run each example, then change one name or number and run it again.
Features on different scales confuse distance-based models. Min-max scaling maps each column onto 0 to 1.
Fit the scale on the training rows only, then apply the same low and high to the test rows.
Example 1: Start here
A column that is already 0 or 1 does not need this step.
vals = [10, 20, 40] # a list; the values stay in this order
lo, hi = min(vals), max(vals) # one value for each name on the left
print([round((v - lo) / (hi - lo), 2) for v in vals]) # in is True when the value is found
Example 2: Add two rows
Scale applies one operation to every number. zip pairs the rows, and the list keeps each sum.
left = [1, 2, 13] # 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, 13, 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, 13, 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
A column that is already 0 or 1 does not need this step.
