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3. Mean Squared Error
easy
Googleentry
Implement a function that computes the Mean Squared Error (MSE) between two NumPy arrays: ground-truth values (y_true) and predicted values (y_pred).
The MSE is defined as the average of the squared differences between corresponding elements:
Your function should return the MSE as a Python float.
Input Signature
| Argument | Type |
|---|---|
| y_pred | np.ndarray |
| y_true | np.ndarray |
Output Signature
| Return Name | Type |
|---|---|
| value | float |
Constraints
Inputs are NumPy arrays of
dtype=floatReturn value must be a Python
float
Hint 1
Compute squared error efficiently with vectorization: (y_true - y_pred) ** 2, then take np.mean(...).
Roles
Data Scientist
ML Engineer
Companies
Google
UberLevels
entry
Tags
numpy
12 people are solving this problem
Python EditorLn 1, Col 1