Definition
In AI, Objective Function which is also referred to as utility function or cost function, is a function that a model seeks to minimize (or maximize) during training. It captures the gap between the model's predictions and the actual values, giving us information about the model's performance [1].
The
"objective function" is the function that you want to minimize or maximize
while solving a problem. In machine learning, let’s say you define a model, M.
To train M, you usually define a loss function L (e.g., a mean squared error),
which you want to minimize. In this instance, L is the "objective
function" of your problem, which you should aim to minimize.
For a better understanding, let’s take a look at an example, involving a fuel distribution optimization problem. You define a function D, which represents the "delivery time" of fuel tankers to filling stations. That is, a function which sums up the travel time between all depot locations and petrol stations. In this scenario, the "objective" of your problem is to minimize this function D, because, essentially, you want to ensure rapid fuel supply, which is associated with either a local (or global) minimum of D. This function D is the "objective function".
An illustration
of objective function [2].Origin
The concept of an objective function originated from the field of mathematical optimization, where it was first developed to measure and improve specific goals in mathematical models. The term grew in importance in the field of AI as the need for formalizing optimization criteria became necessary for training and refining complex systems [3].
Context and Usage
In machine
learning, objective functions act as a road map, helping models achieve better
performance across a number of applications in various domains such as the
following;
- Autonomous Vehicle Navigation: Objective functions reward actions that obey traffic rules and prevents accidents in self-driving cars. Vehicles learn safe and efficient navigation through complex environments using this framework, which represents a major breakthrough in autonomous technology.
- Gaming: Also, objective functions define the reward system in video games that involve reinforcement learning scenarios. Successful actions increase the game score, enabling the AI to learn strategies that boost its score, resulting to more advanced gameplay.
- Predictive Modeling in Healthcare: Objective functions like Log Loss are vital in building models that predict patient outcomes using clinical data. For example, predicting the possibility of a disease reoccurring helps healthcare providers to tailor treatment plans to individual patients, drastically improving patient care [4].
Why it Matters
The objective
function matters a lot in the field of optimization, enabling us to define and
prioritize our objectives, guiding us towards better decisions. By carefully measuring
these objectives, we can thoroughly assess different courses of action,
choosing the one that best meets our goals.
In Practice
A real-life case study of objective function been practiced can be seen in the case of Spotify. Spotify uses objective function in its Discover Weekly recommendation playlist. Spotify realised they needed to change the way they measure user satisfaction. After a lot of hard work and analysis, they discovered 4 main user behaviours (goals), but none of them indicated that the user was truly satisfied, because it varied depending on the goal. To rectify this problem, Spotify built a machine learning model to try to assess user satisfaction with Discover Weekly more accurately. The model took into account 3 things: the user interaction data with Discover Weekly, historical data of how each user’s behaviour changed over the weeks and clustering data that classifies which goal the user belongs to depending on their behaviour [5].
See Also
Related Model
Training and Evaluation concepts:
- Overfitting: Problem where a model learns training data too well and fails to generalize to new data
- Parameters: Adjustable values in a model that are learned during training to make predictions
- Presence Penalty: Parameter that reduces repetition by penalizing tokens that have already appeared
- Pre-Training: Initial training phase where models learn general patterns from large datasets
- Prompt: Input text or instruction given to an AI model to generate a response
References
- Tedai. (2025). Objective Function.
- Botpenguin. (N.D). Objective Function: Maximum Profit & Minimize Cost.
- Lark Editorial Team. (2023). Objective Function.
- Deepgram. (2025). Objective Function.
- Stål, O. (2019). How Spotify knows what music you like (hint: with machine learning).