Understanding Loss Functions in Machine Learning
What is a Loss Function? A loss function, also known as a cost function or error function, quantifies how well a machine learning model's predictions match the actual outcomes. Essentially, it measures the difference between the predicted values and the actual values. The goal of training a machine learning model is to minimize this loss function, thereby improving the model's accuracy. Why are Loss Functions Important? Loss functions are crucial because they: Guide the Training Process : They provide a signal that indicates how well or poorly the model is performing. Influence Model Performance : The choice of loss function can impact how the model trains and ultimately performs. Determine Model Optimization : During training, optimization algorithms use the loss function to adjust the model parameters to minimize the loss. Types of Loss Functions Mean Squared Error (MSE) Mean Squared Error is one of the most common loss functions for regression tasks. It calculates the aver...