Compute the mean squared error between predicted and target values:
MSE=N1i=1∑N(y^i−yi)2Here, N is the number of values, y^i is prediction i, and yi is its target. Return the result as a Python float.
Input: y_pred = [1.1, 2.1, 2.9, 4.2, 4.8], y_true = [1, 2, 3, 4, 5]
Output: 0.022
Explanation: The five squared errors sum to 0.11, which gives a mean of 0.022.
Input: y_pred = [1, 2, 3, 4], y_true = [1, 2, 3, 4]
Output: 0
Input: y_pred = [10.5, 19.5, 30.2], y_true = [10, 20, 30]
Output: 0.18
Convert both inputs with np.asarray(..., dtype=float).
Use np.mean((predictions - targets) ** 2) for the reduction.
Accepts: array
Accepts: array
Compute the mean squared error between predicted and target values:
MSE=N1i=1∑N(y^i−yi)2Here, N is the number of values, y^i is prediction i, and yi is its target. Return the result as a Python float.
Input: y_pred = [1.1, 2.1, 2.9, 4.2, 4.8], y_true = [1, 2, 3, 4, 5]
Output: 0.022
Explanation: The five squared errors sum to 0.11, which gives a mean of 0.022.
Input: y_pred = [1, 2, 3, 4], y_true = [1, 2, 3, 4]
Output: 0
Input: y_pred = [10.5, 19.5, 30.2], y_true = [10, 20, 30]
Output: 0.18
Convert both inputs with np.asarray(..., dtype=float).
Use np.mean((predictions - targets) ** 2) for the reduction.
Accepts: array
Accepts: array