Intro

Inverse Transform After Prediction

~10 mincode completion

Implement inverse_standardize(predictions, mu, sigma) that maps scaled predictions back to the original scale.

Examples

Scaled 0 maps to mu, +1 maps to mu+sigma, -1 maps to mu-sigma

Input
inverse_standardize([0, 1, -1], 100, 20)
Output
[100, 120, 80]

Identity case: sigma=1, mu=0 leaves predictions unchanged

Input
inverse_standardize([3, -2, 0.5], 0, 1)
Output
[3, -2, 0.5]

All-zero predictions map to mu regardless of sigma

Input
inverse_standardize([0, 0], 50, 10)
Output
[50, 50]

Hints

Hint 1

Convert the input with before doing elementwise work.

Hint 2

A common slip here: subtracted mu instead of adding.

Requirements

  • predictions: 1D array of standardized predictions

  • mu: Mean used during forward standardization

  • sigma: Std dev used during forward standardization

  • Return 1D float array in the original scale.

Constraints

  • Allowed library: NumPy only

  • Time limit: 200 ms, Memory: 64 MB

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Where this shows up

~10 min

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Python
import numpy as np

def inverse_standardize(predictions: np.ndarray, mu: float, sigma: float) -> np.ndarray:
    """
    Reverse the standardization: y_original = y_scaled * sigma + mu.

    Args:
        predictions: 1D array of standardized predictions
        mu:          Mean used during forward standardization
        sigma:       Std dev used during forward standardization

    Returns:
        1D float array in the original scale.
    """
    # YOUR CODE HERE
    pass
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