MLPRegressor on the California housing dataset.

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 Let’s carefully evaluate the options for training an MLPRegressor on the California housing dataset.

Options Analysis

  1. “Increasing the number of hidden layers always improves regression accuracy.”
    ❌ Incorrect.

  1. “Using the ReLU activation function in hidden layers is a good choice for MLP regression.”
    ✅ Correct.

  • ReLU is a common, effective choice for hidden layers.

  • It helps avoid vanishing gradient problems and speeds up convergence.

  • In MLPRegressor, the default is 'relu'.

  1. “The output layer should use a softmax activation to predict continuous house prices.”
    ❌ Incorrect.

  • Softmax is for classification problems (outputs probabilities over classes).

  • For regression, the output layer is typically linear (identity activation).

  1. “MLPRegressor does not require feature scaling since neural networks automatically normalize input data.”
    ❌ Incorrect.

  • Neural networks do not automatically normalize input data.

  • Feature scaling (e.g., StandardScaler/MinMaxScaler) is essential for stable training.

✅ Correct Answer

Using the ReLU activation function in hidden layers is a good choice for MLP regression.

Would you like me to also explain what the output activation is by default in MLPRegressor (and why)?

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