Apply MinMaxScaler to column 0

-

 Great question! Let's carefully analyze the preprocessing step by step.

Dataset

X = np.array([
    [2.0, 'apple'],
    [5.0, 'banana'],
    [1.0, 'apple'],
    [4.0, 'cherry']
])

  • Column 0 = numerical values: [2.0, 5.0, 1.0, 4.0]

  • Column 1 = categorical values: ['apple', 'banana', 'apple', 'cherry']

Step 1: Apply MinMaxScaler to column 0

MinMaxScaler scales values to range [0,1]:

x′=x−minmax−minx' = \frac{x - \text{min}}{\text{max} - \text{min}}

  • min = 1.0

  • max = 5.0

So, scaled values:

  • For 2.0 → (2−1)/(5−1)=1/4=0.25(2-1)/(5-1) = 1/4 = 0.25

  • For 5.0 → (5−1)/(5−1)=1(5-1)/(5-1) = 1

  • For 1.0 → (1−1)/(5−1)=0(1-1)/(5-1) = 0

  • For 4.0 → (4−1)/(5−1)=3/4=0.75(4-1)/(5-1) = 3/4 = 0.75

So numerical column becomes: [0.25, 1, 0, 0.75]

Step 2: Apply OneHotEncoder to column 1

Unique categories: ['apple', 'banana', 'cherry']

  • "apple" → [1, 0, 0]

  • "banana" → [0, 1, 0]

  • "cherry" → [0, 0, 1]

Step 3: Combine results for the first row

First row = [2.0, 'apple']

  • Scaled numeric = 0.25

  • OneHot("apple") = [1, 0, 0]

Final transformed row = [0.25, 1, 0, 0]

✅ Correct Answer: Option 1 → [0.25, 1, 0, 0]

Would you like me to also break this into a small table showing how all rows look after transformation (not just the first)?

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