02 Jul 2021

Loss Function

→ The loss function is like a scorekeeper that helps the model learn from its mistakes and improve its performance over time. → It measures how close the model's predictions are to the correct answers. It calculates a value that represents the difference between the predicted answers and the actual answers. This value is the "loss." → As the model gets better and better at its task, the loss decreases. This means the model's predictions become more accurate and reliable.

Example: Mean Squared Error (MSE)

The MSE loss measures the average squared difference between the predicted and actual values. It's often used in regression problems.

def mean_squared_error(y_true, y_pred):
    return np.mean((y_true - y_pred) ** 2)