Loss Functions for Comparing Predictions and Actual Values
A loss function is a function that quantitatively measures the difference between the values predicted by a machine learning model and the actual values.
It serves as a benchmark for evaluating how accurate a model's predictions are by calculating how different the predictions are from the actual values.
The learning objective of a machine learning model is to minimize the loss, which is calculated by the loss function.
As the loss decreases, the model's predictions align more closely with the actual values, thereby improving its performance.
Role of the Loss Function
During training, the loss function is used to evaluate the accuracy of predictions and to adjust the weights and biases in order to reduce the loss.
Initially, the model generates predictions, and then the loss function is used to calculate the difference between predictions and actual values.
If this difference is large, the weights and biases are adjusted to make better predictions.
This process is repeated, leading to a gradual reduction in the loss value and enabling the model to learn to make increasingly accurate predictions.
In other words, the goal of a machine learning model is to minimize the loss calculated by the loss function, thereby enhancing the model's prediction accuracy.
Types of Loss Functions
Different loss functions are used depending on the type of problem.
Loss Functions Used in Regression Problems
In regression problems, the difference between predictions and actual values is calculated in continuous numbers.
Examples of regression problems include predicting stock prices, housing prices, and more.
The most widely used loss functions in these cases are Mean Squared Error (MSE) and Mean Absolute Error (MAE).
Mean Squared Error
Mean Squared Error is calculated by squaring the difference between predictions and actual values and then taking the average.
Because the differences are squared, larger differences are penalized more heavily.
Thus, the greater the discrepancy between the predicted and actual values, the higher the loss value, prompting adjustments in weights during training to reduce the loss.
Mean Absolute Error
Mean Absolute Error is calculated by converting the difference between predictions and actual values to absolute values and then taking the average.
Because it uses absolute values, it's less sensitive to outliers and beneficial for uniformly reducing errors of consistent magnitude in machine learning models.
Loss Functions Used in Classification Problems
In classification problems, the model needs to predict the probability that the inputs belong to a particular class.
Examples include spam mail classification, sentiment analysis, and more.
The most commonly used loss function here is Cross-Entropy Loss.
This function evaluates how accurately the model predicts the correct answer based on probability values.
Designed to decrease as the model's probability of predicting the correct class increases, it lowers the loss value as the model outputs high probabilities for the correct class and increases it for incorrect ones.
The learning process of a machine learning model is essentially the process of reducing the loss value calculated by the loss function, and as this loss decreases, the model's performance improves.
Subsequent lessons will provide a detailed introduction to the loss functions and optimization algorithms, such as Gradient Descent.
In the next lesson, we will explore the Cost Function.
Want to learn more?
Join CodeFriends Plus membership or enroll in a course to start your journey.