Understanding MinMaxScaler in Scikit-learn with a Multiple Choice Example

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When building machine learning models, preprocessing numerical data is just as important as handling categorical features. One widely used technique is feature scaling, and Scikit-learn provides utilities like MinMaxScaler and StandardScaler for this purpose. Let’s break down a multiple-choice question based on MinMaxScaler and also understand how to approach similar questions.

The Code Example

from sklearn.preprocessing import MinMaxScaler, StandardScaler

data = [[5, 2], [8, 3], [2, 4], [6, 5], [4, 6]]

scaler = MinMaxScaler()
scaler.fit(data)
print(scaler.data_max_)

Step-by-Step Explanation

  1. Dataset Preparation

    data = [[5, 2], [8, 3], [2, 4], [6, 5], [4, 6]]

    • The dataset has 5 samples and 2 features.

    • Feature 1 values: [5, 8, 2, 6, 4]

    • Feature 2 values: [2, 3, 4, 5, 6]

  2. MinMaxScaler Initialization

    scaler = MinMaxScaler()

    • MinMaxScaler transforms features by scaling each one to a given range (default: [0, 1]).

    • It uses the formula:

      Xscaled=X−XminXmax−XminX_{scaled} = \frac{X - X_{min}}{X_{max} - X_{min}}

  3. Fitting the Scaler

    scaler.fit(data)

    • During fitting, the scaler calculates:

      • data_min_: Minimum value per feature.

      • data_max_: Maximum value per feature.

    Let’s calculate manually:

    • Feature 1 → min = 2, max = 8

    • Feature 2 → min = 2, max = 6

    So:

    • data_min_ = [2, 2]

    • data_max_ = [8, 6]

  4. Printing Maximum Values

    print(scaler.data_max_)

    • Output: [8, 6]

Correct Answer

The output will be:

[8, 6]

How to Approach Similar Questions

When asked about preprocessing objects like MinMaxScaler, StandardScaler, or OneHotEncoder, the approach is systematic:

  1. Understand the Dataset Structure
    Identify how many features (columns) and samples (rows) exist.

  2. Know What the Method Stores

    • MinMaxScaler stores data_min_, data_max_, data_range_.

    • StandardScaler stores mean_, var_, scale_.

    • OneHotEncoder stores unique categories per feature.

  3. Do Manual Calculations
    Work out min, max, mean, or variance by hand for each feature.

  4. Map to the Question
    Look at what attribute is being asked (data_max_, mean_, .transform(data), .shape), and return the result accordingly.

Key Takeaways

  • MinMaxScaler rescales features into a specific range.

  • data_min_ and data_max_ are computed directly from the dataset.

  • For similar MCQs, always:

    1. Break the data into features.

    2. Compute required statistics (min, max, mean, variance).

    3. Match with the attribute being accessed.

✅ Final Answer: [8, 6]

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