Loss Function In Machine Learning
In Loss Function In machine learning , a loss function is a measure of how well a machine learning model's predictions align with the true values or labels of the training data. The loss function quantifies the "loss" or error between the predicted values and the actual values, and it serves as the basis for training the model to minimize this error. The choice of a loss function depends on the specific task and the nature of the data. Different machine learning problems, such as classification, regression, and sequence generation, often require different loss functions. Here are some commonly used loss functions: Mean Squared Error (MSE): It is a popular loss function for regression problems. It measures the average squared difference between predicted and actual values. MSE formula is: MSE = (1/n) * Σ(yᵢ - ŷᵢ)² Where yᵢ represents the actual value, ŷᵢ represents the predicted value, and n is the total number of samples. Binary Cross-Entropy: This loss function is comm