🧠 Understanding Hidden Layers in MLPRegressor

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The MLPRegressor in scikit-learn is used to build feedforward neural networks for regression problems. A crucial parameter is hidden_layer_sizes, which controls how many hidden layers exist and how many neurons each contains.

📌 Question Recap

The given code:

regr = MLPRegressor(hidden_layer_sizes=(5,3), max_iter=5).fit(X_train, y_train)

Options included statements about the number of hidden layers and neurons.

✅ Step-by-Step Breakdown

1. hidden_layer_sizes=(5,3)

  • This tuple defines the architecture of the hidden layers.

  • Each number corresponds to the number of neurons in that layer.

  • (5, 3) → means:

    • First hidden layer → 5 neurons

    • Second hidden layer → 3 neurons

Thus, the model has 2 hidden layers in total.

2. max_iter=5

  • This sets the maximum number of training iterations.

  • Here, training will stop after 5 iterations (very small, usually used just for demonstration).

  • It does not affect the architecture, only training duration.

❌ Incorrect Options

  • Option 1: "3 hidden layers with 5 neurons in each" → ❌ Wrong, because we only have 2 hidden layers.

  • Option 2: "5 hidden layers with 3 neurons in each" → ❌ Wrong, same reason.

  • Option 5: "None of the given options are correct" → ❌ Wrong, because correct statements exist.

✅ Correct Options

  • Option 3: ✔ "The neural network contains 2 hidden layers with 3 neurons in the second hidden layer"

  • Option 4: ✔ "The neural network contains 2 hidden layers with 5 neurons in the first hidden layer"

Both perfectly describe the architecture defined by (5,3).

🎯 Final Answer

👉 The correct statements are:

  • ✅ The neural network contains 2 hidden layers with 3 neurons in the second hidden layer

  • ✅ The neural network contains 2 hidden layers with 5 neurons in the first hidden layer

✨ Takeaway:

  • hidden_layer_sizes=(x,y,...) → Each entry = number of neurons in that hidden layer.

  • Tuple length = number of hidden layers.

  • max_iter controls training, not architecture.

Would you like me to also draw a simple neural network diagram (input layer → hidden layers → output) for (5,3) so it’s more visual?

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