Building Custom Loss Functions: Top 3 Considerations

One of the most important core elements of a machine learning model is the loss function, which is also called the cost function. In data science, the loss function is used to determine how well a machine learning model performs for a single observation in a dataset—the smaller the loss, the better the model’s accuracy. To visualize, loss function is your map when you’re at the top of the mountain, and you’re looking for a way down the foothill. In other words, loss functions help reduce error to get as close to the machine’s intended output. 

Custom Loss Functions

Loss functions that are frequently used in the industry are easy to understand and are built into well-known deep learning frameworks, like Keras or Phyton. These built-in loss functions are effective for most common tasks, such as classification and regression. However, there are some activities that cannot be handled well using these built-in loss functions, hence it needs the use of other losses that are more fit for that specific task. For this reason, a custom loss function is created, which evaluates the difference between the predicted and actual values depending on custom criteria. 

Here are rules to follow when creating a custom loss:

• The loss function should only accept two arguments: the target value and the predicted value. You’ll need these values since two quantities are required to calculate the prediction error (loss).
• The loss function must use the predicted value when calculating the loss. Otherwise, the gradient expression won’t be defined, and you’ll receive an error.
• Then, simply compile this function into the model.

Considerations When Building Custom Loss Function

If you’re still not convinced that custom loss functions will help, here are three reasons why you should consider building custom loss functions:

1. Take Arguments And Returns Scalars

You can create your own custom loss function, but it’s also good to know existing loss functions in deep learning frameworks like Keras. A custom loss function in Keras can improve the performance of a machine learning model in the ways that you want, and it can be highly beneficial for fixing specific issues more quickly. Supposed you’re developing a model for investment portfolio optimization. In this scenario, creating a custom loss function with a huge penalty for predicting price moves in the wrong direction is suitable. 

If you’re using Keras, you can write a custom loss function that takes two arguments and returns a scalar—the true value and the predicted value. The custom loss function is then passed to the model. After that, compile the custom loss function as a parameter, just like any other loss function.

To learn more about building custom loss functions in Keras, continue reading this article.

2. Risk Symmetry

Assume you’re at home and need to figure out what time you’ll leave so you can be on time for an interview. You don’t want to leave too soon because you’ll have to wait for the interview and may have to spend money on an expensive cup of coffee while you’re waiting. You also don’t want to arrive too late because you can be turned down since you’re being unprofessional.  Yet, the risk of leaving too soon is much smaller than the risk of leaving too late––the cost of waiting is insignificant compared to the cost of unemployment. This is known as risk asymmetry.

You can implement machine learning to determine when to leave the house. You can do this by addressing the risk asymmetry directly in a ML (machine learning) model by employing a custom loss function, which penalizes late errors more than early errors.

3. Deep Learning

Deep learning may give a simple solution to certain types of challenges during the training of an ML model. Deep learning allows you to explore multiple built-in and custom loss functions that can be utilized with various optimizers supplied rather than building a custom probability function and optimizer. 

Some models learn from chart of inputs and examples, and the loss function used must be relevant to the specific issue, like classification or regression. In this way, custom loss functions can be valuable in deep learning when developing regression models that must make predictions for data of varying orders of magnitude. If this is the case, you can create custom loss functions to predict house prices in an area where values can vary greatly.

Final Thoughts

At times, the more efficient a machine learning model’s prediction doesn’t always mean that it’s better for business. This is because there may be some disorder between data science metrics and business metrics. As a result, a custom loss function is important in driving the machine learning model to the same goal as the business’s goal. 

As you can see, applying custom loss functions to your machine learning model can improve the performance of a machine learning model in any way you want, making it more helpful in tackling specific issues in machine learning.