# Loss Functions

**- Overview**

A loss function is a mathematical function that measures the difference between the predicted value of a machine learning (ML) model and the actual value. It is also called the error function.

Loss function is very important in ML or deep learning. A loss function acts as feedback to the system being built. Without feedback, the system will not know what and where to improve.

Suppose you are solving any problem and you have trained a ML model on a dataset and are ready to present it to the client. But how can you be sure that your model will give the best results? Is there a metric or technique that can help you quickly evaluate a model on a dataset? Yes, loss functions play a role in machine learning or deep learning.

The loss function measures the performance of the model and guides the optimization process. It defines how far the model's prediction is from the target value. The choice of loss function is determined by the purpose of the model.

Loss function is often used interchangeably with cost function. However, the cost function is defined as the average loss function over all training examples.

**- Categories of Loss Functions**

Loss functions can be divided into two major categories: regression loss and classification loss.

Here are some AI loss functions:

- Mean squared error: Also known as L2 Loss, this loss function is used for regression. It represents the difference between the original and predicted values.
- Hinge loss: This loss function is used in classification, usually in SVM. It penalizes predictions that are incorrect or not confident.
- Huber loss: This loss function is used in regression machine learning tasks. It is less sensitive to outliers, but also penalizes minor errors.
- Categorical cross entropy: This loss function is used in multi-class classification tasks. It is similar to binary cross-entropy, but with more classes.
- L1 loss: This loss function combines the benefits of MSE loss and MAE loss. It is less sensitive to outliers than the mean square error loss.
- Mean bias error: This loss function takes the actual difference between the target and the predicted value. It is not one of the more commonly used loss functions.
- Dice loss: This loss function is commonly used in image segmentation tasks. It measures the overlap between the predicted and true segmentation masks.

**- Loss Functions in AI Regression**

A loss function in AI regression is a mathematical function that measures how well a ML model fits a data set. It quantifies the difference between the model's predicted and actual values.

A loss function summarizes the model's under- and overestimations into a single number, called the prediction error. The loss is a number that indicates how bad the model's prediction was on a single example. If the model's prediction is perfect, the loss is zero.

The goal of training a model is to find a set of weights and biases that have low loss, on average, across all examples.

The mean squared error (MSE) loss function is considered the best loss function for regression problems. This is when the neural network is predicting a continuous scalar value.

Most loss functions apply to regression and classification ML problems.

**- Loss Functions in AI Classification**

In machine learning, classification loss functions represent the cost of inaccurate predictions in classification problems.

Loss functions, also known as error functions, measure the difference between the prediction output and the target value. They determine a model's performance by comparing the distance between the prediction output and the target values.

The cross-entropy loss function is commonly used for classification tasks. It increases as the predicted probability diverges from the actual label. It measures the performance of a classification model whose predicted output is a probability value between 0 and 1.

In a machine learning regression task, loss functions such as mean squared error or mean absolute error are better suited.

**[More to come ...]**