Linear RegressionIntro
Compute Mean Squared Error
~8 mincode completion
Implement using NumPy. Do not use a for loop.
Examples
Perfect predictions → MSE = 0
- Input
- mean_squared_error([1, 2, 3], [1, 2, 3])
- Output
- 0
Known MSE value
- Input
- mean_squared_error([1, 2, 3, 4], [2, 2, 2, 2])
- Output
- 1.5
Single element
- Input
- mean_squared_error([5], [3])
- Output
- 4
Hints
Hint 1
does the sum and the division in one step.
Hint 2
Do not forget to square. That step is easy to skip.
Requirements
y_true: Ground truth values, shape (n,)y_pred: Model predictions, shape (n,)Return Scalar MSE value.
Constraints
Allowed library: NumPy only
Time limit: 200 ms, Memory: 64 MB
Where this shows up
~8 min
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Python
import numpy as np
def mean_squared_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
"""
Compute mean squared error between targets and predictions.
Args:
y_true: Ground truth values, shape (n,)
y_pred: Model predictions, shape (n,)
Returns:
Scalar MSE value.
"""
# YOUR CODE HERE
pass