MLPClassifier

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 Great question! Let’s carefully analyze this one.

Problem

You’re training an MLPClassifier (a neural network) on MNIST.

Option Analysis

  1. Convert images to grayscale.
    ❌ Not needed — MNIST is already grayscale.

  2. Scaling the data using Min-Max Scaling.
    ✅ Correct — Neural networks (like MLP) work best when inputs are normalized/scaled (e.g., in [0,1] or mean 0, variance 1). This is essential for faster convergence and better performance.

  3. Apply PCA to reduce feature dimensions.
    ⚠️ Not essential — PCA can help with speed but is not required for performance; MNIST features are manageable (784).

  4. One-Hot encode the target labels (digits 0–9).
    ⚠️ If you use MLPClassifier from Scikit-Learn, it does not require one-hot encoding (it accepts integer class labels directly). So this is not essential.

✅ Correct Answer:

Scaling the data using Min-Max Scaling

👉 Would you like me to also explain why scaling is more important than PCA for neural nets like MLP?

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