This chapter is from the book
This chapter is from the book
This chapter is from the book
What Is a Model?
Regardless of the goal or the technique used, machine learning involves algorithms that learn from data how to reach the right conclusion. When AI systems are developed, designers need to represent the relationship between the input data provided to the program and the output it produces. This relationship is known as the model. The learning element of the process depends on the task at hand. In all cases, there needs to be a human to design the learning program: what the program should look like, what the machine should learn, and how the learning should happen. To build this structure, AI scientists need to represent in some way the expected relationship between the data that will be provided to the program and the output expected from the program. More concretely, an AI/machine learning model is a mathematical representation (or program) that identifies these patterns and relationships, enabling it to make predictions or decisions without needing explicit instructions. The model ingests data, processes it based on the learned relationships, and outputs predictions, classifications, or representations.
Models are trained on data to optimize their internal parameters. These parameters are numeric values that become accurate over time as the model goes through successive training cycles. The type of model and its learning method are chosen based on the task, such as classification, regression, or content generation. In some cases, the relationship between input and output can be expressed mathematically, like a simple linear equation. In other cases, it might be captured in code. Both serve the same purpose: They represent a logical method that allows the model to connect data to decisions and, ultimately, predict an outcome.
For example, imagine that you are working for a road safety organization, and your goal is to estimate how long it takes a car to stop after a particular curve on the road. Many variables influence stopping time, such as speed, weight, and road conditions, but let’s start by modeling the relationship between just one set of variables: the car’s initial speed and its stopping time. This is a regression problem: Your goal is to predict a continuous value (time to stop). If there is a direct, proportional (linear) relationship between speed and stopping time, the model might use a linear equation, like this:
y = ax + b
where:
x is the input variable (car speed)
y is the output variable (stopping time)
a is the slope (how much y changes for each unit increase in x)
b is the intercept (the value of y when x = 0)
In many real-world examples, the value of b might be zero (because, for example, a stopped car does not need any time to stop). In other cases, such as sales forecasting based on advertisement investment, the intercept could reflect baseline sales even when advertising spending (x) is zero. Each data point you collect, such as car speed and its corresponding stopping time, helps the model learn the shape of this relationship. When plotted, the curve is often visualized as a line: simple, interpretable, and in this case useful for building intuition about how linear regression works.
In the real world, most problems involve far more than one input variable. For example, stopping typically involves many factors, such as weather conditions, tire quality, and the weight of the car. This creates a multidimensional problem, with each input variable referred to as a feature. The features are usually represented as x1, x2,…xn, each with its own coefficient (the number in front of the variable). The model equation generalizes to:
y = a1x1 + a2 x2 + … + an xn + b
The process of deciding which features to include and which ones to ignore is known as feature engineering. It is a critical step in supervised learning. More features can make the model more powerful and accurate, but they also make it more complex, harder to interpret, and slower to train. As in many other parts of AI, deciding the number of features for a particular problem is often a balancing act.
Of course, not all models are linear. Many models are nonlinear or even composed of multiple sub-models or equations. This complexity arises when the output is the result of multiple interacting factors. Imagine testing stopping times on a frozen lake. Some cars might skid as the tires lock, and stopping time could be affected by brake pad pressure, tire heat, car weight, and how the ice responds to heat and pressure. The model might need to calculate intermediate values like heat buildup or skid length before combining them into a final output. In such cases, the model could consist of multiple equations, some feeding into others. This is where composite models and deep learning architectures come into play, with layers of computations leading from input to output.
