Understanding Loss Functions to Maximize Machine Learning Model Performance (Updated 2023)
Picture this – you’ve trained a machine learning model on a given dataset and are ready to put it in front of your client. But how can you ensure this model will give the optimum result? Is there a metric or technique that will help you quickly evaluate your model on the dataset? Yes – and that, in a nutshell, is where loss functions come into play in machine learning.
In this article, I will discuss 7 common loss functions used in machine learning and explain where each of them is used. We have much to cover in this article, so let’s begin!
- Understand different loss functions in Machine Learning.
- Know the difference between loss function and cost function.
- Learn how to implement different loss functions in Python.
Loss functions are one part of the entire machine-learning journey you will take. Here’s the perfect course to help you get started and make you industry-ready:
Table of contents
What Are Loss Functions?
Loss functions are at the heart of the machine learning algorithms we love to use. But I’ve seen the majority of beginners and enthusiasts in deep learning and machine learning becoming quite confused regarding how and where to use them. They’re not difficult to understand and will enhance your understanding of machine learning algorithms infinitely. So, what are loss functions, and how can you grasp their meaning?
Let’s say you are on the top of a hill and need to climb down. How do you decide which way to take?
Here’s what I would do:
- Look around to see all the possible paths.
- Reject the ones going up. This is because these paths would actually cost me more energy and make my task even more difficult.
- Finally, take the path that I think has the most slope downhill.
This intuition that I just judged my decisions against? This is exactly what a loss function provides.
A loss function maps decisions to their associated costs.
Deciding to go up the slope will cost us energy and time. Deciding to go down will benefit us. Therefore, it has a negative cost.
In supervised machine learning algorithms, we want to minimize the error for each training example during the learning process, i.e., we want the loss value obtained from the loss function to be as low as possible. This is done using some optimization strategies like gradient descent. And this error comes from the loss function.
Difference Between Loss Function and Cost Function
I want to emphasize this: although cost function and loss function are synonymous and used interchangeably, they are different.
A loss function is for a single training example. It is also sometimes called an error function. A cost function, on the other hand, is the average loss over the entire training dataset. The optimization strategies aim at minimizing the cost function.
What Are Regression Loss Functions?
You must be quite familiar with linear regression at this point. It deals with modeling a linear relationship between a dependent variable, Y, and several independent variables, X_i’s. Thus, we essentially fit a line in space on these variables.
Y = a0 + a1 * X1 + a2 * X2 + ....+ an * Xn
We will use the given data points to find the coefficients a0, a1, …, an.
We will use the famous Boston Housing Dataset to understand this concept. And to keep things simple, we will use only one feature – the Average number of rooms per dwelling (X) – to predict the dependent variable – the Median Value (Y) of houses in $1000′s.
We will use Gradient Descent as an optimization strategy to find the regression line. I will not go into the intricate details about Gradient Descent, but here is a reminder of the Weight Update Rule:
Here, theta_j is the weight to be updated, alpha is the learning rate, and J is the cost function. The cost function is parameterized by theta. Our aim is to find the value of theta, which yields minimum overall cost.
You can get an in-depth explanation of Gradient Descent and how it works here.
I have defined the steps that we will follow for each loss function below:
- Write the expression for our predictor function, f(X), and identify the parameters that we need to find
- Identify the loss to use for each training example
- Find the expression for the Cost Function – the average loss on all examples
- Find the gradient of the Cost Function with respect to each unknown parameter
- Decide on the learning rate and run the weight update rule for a fixed number of iterations
Here are the different types of regression loss functions.
Mean Squared Error Loss
Squared Error loss for each training example, also known as L2 Loss, is the square of the difference between the actual and the predicted values:
The corresponding cost function is the Mean of these Squared Errors, which is the Mean Squared Error (MSE).
I encourage you to try and find the gradient for gradient descent yourself before referring to the code below.
I used this code on the Boston data for different values of the learning rate for 500 iterations each:
Let’s talk a bit more about the MSE loss function. It is a positive quadratic function (of the form ax^2 + bx + c where a > 0). Remember how it looks graphically?
A quadratic function only has a global minimum. Since there are no local minima, we will never get stuck in one. Hence, it is always guaranteed that Gradient Descent will converge (if it converges at all) to the global minimum.
The MSE loss function penalizes the model for making large errors by squaring them. Squaring a large quantity makes it even larger, right? But there’s a caveat. This property makes the MSE cost function less robust to outliers. Therefore, it should not be used if our data is prone to many outliers.
Mean Absolute Error Loss
Absolute Error for each training example is the distance between the predicted and the actual values, irrespective of the sign, i.e., it is the absolute difference between the actual and predicted values. Absolute Error is also known as the L1 loss:
As I mentioned before, the cost is the Mean of these Absolute Errors, which is the Mean Absolute Error (MAE).
The MAE cost is more robust to outliers as compared to MSE. However, handling the absolute or modulus operator in mathematical equations is not easy. I’m sure a lot of you must agree with this! We can consider this as a disadvantage of MAE.
Here is the code for the update_weight function with MAE cost:
We get the below plot after running the code for 500 iterations with different learning rates:
The Huber loss combines the best properties of MSE and MAE. It is quadratic for smaller errors and is linear otherwise (and similarly for its gradient). It is identified by its delta parameter:
Binary Classification Loss Functions
The name is pretty self-explanatory. Binary Classification refers to assigning an object to one of two classes. This classification is based on a rule applied to the input feature vector. These loss functions are used with classification problems. For example, classifying an email as spam or not spam based on, say, its subject line is a binary classification.
I will illustrate these binary classification loss functions on the Breast Cancer dataset.
We want to classify a tumor as ‘Malignant’ or ‘Benign’ based on features like average radius, area, perimeter, etc. For simplification, we will use only two input features (X_1 and X_2), namely ‘worst area’ and ‘mean symmetry,’ for classification. The target value Y can be 0 (Malignant) or 1 (Benign).
Here is a scatter plot for our data:
Here are the different types of binary classification loss functions.
Binary Cross Entropy Loss
Let us start by understanding the term ‘entropy’. Generally, we use entropy to indicate disorder or uncertainty. It is measured for a random variable X with probability distribution p(X):
The negative sign is used to make the overall quantity positive.
A greater value of entropy for a probability distribution indicates a greater uncertainty in the distribution. Likewise, a smaller value indicates a more certain distribution.
This makes binary cross-entropy suitable as a loss function – you want to minimize its value. We use binary cross-entropy loss function for classification models, which output a probability p.
Probability that the element belongs to class 1 (or positive class) = p Then, the probability that the element belongs to class 0 (or negative class) = 1 - p
The range of the sigmoid function is [0, 1] which makes it suitable for calculating probability.
Try to find the gradient yourself and then look at the code for the update_weight function below.
I got the below plot using the weight update rule for 1000 iterations with different values of alpha:
Hinge loss is primarily used with Support Vector Machine (SVM) Classifiers with class labels -1 and 1. So make sure you change the label of the ‘Malignant’ class in the dataset from 0 to -1.
Hinge Loss not only penalizes the wrong predictions but also the right predictions that are not confident.
Hinge loss for an input-output pair (x, y) is given as:
After running the update function for 2000 iterations with three different values of alpha, we obtain this plot:
Multi-Class Classification Loss Functions
Emails are not just classified as spam or not spam (this isn’t the 90s anymore!). They are classified into various other categories – Work, Home, Social, Promotions, etc. This is a Multi-Class Classification use case.
We’ll use the Iris Dataset to understand the remaining two loss functions. We will use 2 features X_1, Sepal length, and feature X_2, Petal width, to predict the class (Y) of the Iris flower – Setosa, Versicolor, or Virginica.
Our task is to implement the classifier using a neural network model and the in-built Adam optimizer in Keras. This is because as the number of parameters increases, the math, as well as the code, will become difficult to comprehend.
If you are new to Neural Networks, I highly recommend reading this article first.
Here is the scatter plot for our data:
Here are the different types of multi-class classification loss functions.
Multi-Class Cross Entropy Loss
The multi-class cross-entropy loss function is a generalization of the Binary Cross Entropy loss. The loss for input vector X_i and the corresponding one-hot encoded target vector Y_i is:
We use the softmax function to find the probabilities p_ij:
“Softmax is implemented through a neural network layer just before the output layer. The Softmax layer must have the same number of nodes as the output layer.” Google Developer’s Blog
Finally, our output is the class with the maximum probability for the given input.
We build a model using an input layer and an output layer and compile it with different learning rates. Specify the loss parameter as ‘categorical_crossentropy’ in the model.compile() statement:
Here are the plots for cost and accuracy, respectively, after training for 200 epochs:
The Kullback-Liebler Divergence is a measure of how a probability distribution differs from another distribution. A KL-divergence of zero indicates that the distributions are identical.
Notice that the divergence function is not symmetric.
This is why KL-Divergence cannot be used as a distance metric.
I will describe the basic approach of using KL-Divergence as a loss function without getting into its math. We want to approximate the true probability distribution P of our target variables with respect to the input features, given some approximate distribution Q. Since KL-Divergence is not symmetric, we can do this in two ways:
The first approach is used in Supervised learning, and the second in Reinforcement Learning. KL-Divergence is functionally similar to multi-class cross-entropy and is also called relative entropy of P with respect to Q:
We specify the ‘kullback_leibler_divergence’ as the value of the loss parameter in the compile() function as we did before with the multi-class cross-entropy loss.
KL-Divergence is used more commonly to approximate complex functions than in multi-class classification. We come across KL-Divergence frequently while playing with deep-generative models like Variational Autoencoders (VAEs).
Woah! Through this tutorial, we have covered a lot of ground here. Give yourself a pat on your back for making it all the way to the end. This was quite a comprehensive list of loss functions we typically use in machine learning. I would suggest going through this article a couple of more times as you proceed with your machine-learning journey. This isn’t a one-time effort. It will take a few readings and experience to understand how and where these loss functions work.
Meanwhile, you can also check out our comprehensive beginner-level machine learning course:
- Loss functions and cost functions are two different types of functions.
- Loss functions are used to calculate the error of the ML model.
- The selection of the loss function depends on the nature of the problem, i.e., regression or classification.
Frequently Asked Questions
A. Loss functions and activation functions are two different functions used in Machine Learning and Deep Learning. Loss function is used to calculate the error of a model, whereas activation function is used to get the output of a node. MSE and MAE are some examples of loss functions, while ReLU and softmax are activation functions.
A. Here are the different types of loss functions on the basis of regression and classification problems:
– Regression Loss Functions: Mean Squared Error Loss, Mean Absolute Error Loss, and Huber Loss
– Binary Classification Loss Functions: Binary Cross-Entropy and Hinge Loss
– Multi-class Classification Loss Functions: Multi-class Cross Entropy Loss and Kullback Leibler Divergence Loss
A. In binary classification, our output is classified into two classes, whereas in multi-class classification, our output is classified into more than two classes.