Why naive models are still relevant in the age of complex AI

22 Apr

Written by Vilma Ylvén

AI is often seen as black-box complexity, but what if the answer to your problem lies not in sophisticated algorithms, but in simpler approaches? At Algorithma, we champion the power of naive models. Often overlooked due to their basic nature, they offer a surprising set of advantages that can be incredibly valuable for businesses of all sizes.

Naive models are a type of machine learning model that use very basic approaches to make predictions. They are often the simplest form of machine learning models and rely on minimal data manipulation or complex algorithms. They usually have the following key characteristics:

Simple calculations: They involve basic mathematical operations on existing data points, such as averaging past values or using the last observed value as a prediction for the next one.
Limited assumptions: Naive models make minimal assumptions about the underlying structure of the data. They don't try to identify complex relationships between variables.
Interpretability: The logic behind the predictions is clear and easy to understand. This is because they use straightforward calculations.

As illustration, some common examples of naive models include:

Moving average: A prediction of the future value is made by simply averaging a number of past values.

Last value model: Even simpler than the moving average, this model uses the most recent value to make its prediction. For example, a good prediction of tomorrow's temperature can be gotten by simply predicting it will be today’s temperature.
Seasonal naive model: This model considers seasonality by using the value from the same period in the past as a prediction for the current period. Using the same example as above, if you wanted to predict tomorrow's temperature at 13:00 you would use today’s temperature at 13:00 as you estimate.

These methods might feel wholly insufficient at first glance, but there are many reasons why they can be beneficial if it turns out to work.

Speed and efficiency

Time is of the essence. Naive models are significantly simpler to build and implement compared to their intricate counterparts. This translates to quicker development cycles, allowing you to test your ideas faster and gain valuable insights sooner. Imagine you need to predict customer churn (when a customer stops doing business with you). With a naive model, you could analyze a few key data points, like recent purchase history, and build a model in a matter of days. This rapid prototyping allows you to experiment with different approaches and identify potential solutions quickly. Moreover, a simple analysis like this does not require expensive and sometimes unavailable resources like GPUs.

The environmental impact of large AI models shouldn't be ignored. Training them requires enormous amounts of computing power, often relying on powerful GPUs, which translates to a hefty carbon footprint. Choosing a simpler model reduces the training time's environmental footprint, making your AI solution more sustainable. For businesses with sustainability goals, naive models offer a way to leverage AI responsibly, minimizing their environmental impact.

Data

Training complex models often requires massive datasets, a resource that can be scarce. Naive models, however, can excel with smaller datasets, making them ideal for getting started and overcoming data limitations.

Interpretability

One of the inherent weaknesses of large neural networks is that it becomes increasingly impossible for people to understand what happens in them. When it comes to dealing with data, transparency is often an important aspect for businesses to consider. With Naive models is it possible to know exactly what data is used and how, something that is significantly harder with large models. To learn more about interpretability in AI, check out our article on explainable AI.

Benchmarking power

When developing complex AI models, the question of evaluation is ever-present. How can you be certain that the model is good? How to ensure the predictions aren’t just as good as a simple guess? This is where it is important to have a good baseline for comparison! Naive models provide a strong foundation for this. By establishing their performance, you can assess the effectiveness of more complex models you may develop later. This helps you determine if the added complexity is truly justified by a significant improvement in results.

Key considerations for choosing the right model
There's no one-size-fits-all answer to choosing the right model. It's a multi-faceted decision that depends on several key considerations:

Problem complexity: Simpler problems are often well-suited for naive models. For instance, predicting short-term sales trends might be effectively handled by a moving average model. However, more complex problems with intricate relationships between variables might necessitate a more sophisticated approach like a deep learning model.
Data availability: As mentioned earlier, naive models can be a great starting point when data is limited. However, if you have access to a large, high-quality dataset, a more complex model might be able to extract deeper insights and achieve superior performance.
Accuracy requirements: Consider the level of accuracy needed for your specific use case. If a ballpark estimate is sufficient, a naive model might be adequate. However, if highly precise predictions are critical, a more complex model might be necessary.

The land in between

There are of course an endless amount of models for analyzing data, and many that fall into the space between naive models and machine learning. Just because a naive model is too simple it does not necessarily make a machine learning algorithm the best answer for solving the problem. Statistical models like least squares linear regression have been used for centuries and can be used for many types of problems. It is increasingly difficult to determine where the line between a statistical model and a machine learning model goes, but it’s always worth considering if a less complex model can achieve sufficient results for your problem.

It's easy to get caught up in the allure of complex models, but naive models offer a valuable and often overlooked alternative. Their simplicity translates to speed, efficiency, and the ability to leverage smaller datasets. Perhaps most importantly, naive models provide transparency and interpretability, fostering trust and responsible AI development. By understanding the strengths and limitations of both complex and naive models, businesses can make informed decisions and choose the right tool for the job.

Remember, sometimes the simplest solution can be surprisingly powerful.