Senior Data Scientist
Many retailers today are investing in deep learning. However, before taking the plunge, it’s important to first consider where traditional approaches work well.
Deep learning is a major buzzword the retail industry has become infatuated with. Many assume that deep neural networks are the end all be all and should replace traditional algorithms altogether. However, there is no one perfect model to predict every component. Before jumping ship from traditional algorithms, it’s vital to understand how to leverage both types of algorithms to produce more accurate predictions.
Throughout this article, we will learn more about the strengths and weaknesses of regression models (traditional algorithms) and deep neural networks (deep learning).
Before we dive into the pros and cons of each type of model, we first must understand the difference between the two. Both deep neural networks and regression-based models are a form of Artificial Intelligence.
They can independently learn from past decisions and adapt to optimize results. Essentially, they can achieve the same output, but the key difference is how they calculate the optimal solution.
Regression-based models are a form of predictive analytics, falling in the machine learning bucket (as shown in the graphic below). They use a variety of statistical techniques that examine past and current data to make predictions about the future.
Essentially, regression-based models estimate the relationship between a dependent variable and one or more independent variables. Often, regression analysis is used for prediction and forecasting.
According to IBM, Deep neural networks form a subset of machine learning, with algorithms that aim to mimic the structure of the human brain to solve complex problems, which more standard machine learning techniques cannot handle.
“Neural networks rely on training data to learn and improve their accuracy over time. Once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity.”
Overall, the key difference between the two:
While deep learning is a hot topic, it cannot solve all of the problems retailers face within their data science space. Instead, it’s important to recognize the opportunities both deep neural networks and regression models provide.
We believe that deep neural networks offer some big advantages, particularly in pricing, but retailers should still retain a regression-style combination for explainability and reduction of computational complexity.
Cognira is the leading artificial intelligence solutions provider for retailers. Cognira is passionate about helping retailers unlock valuable, transformative business insights from their data.
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