When to Go Deep: Choosing Between Neural Networks and Traditional Machine Learning
One question we see a lot in our work is when to deploy neural networks over more traditional machine learning algorithms. While both approaches can be applied to solve business and scientific problems, neural networks can be involved to build, train, and validate. Some have started treating these tools as do-anything Swiss Army Knives, but the question is worth considering, because a neural network may be overkill for your application.
Neural networks, particularly deep learning models, excel in scenarios where the data is complex, high-dimensional, and exhibits intricate patterns that may not be easily discernible through conventional means. Tasks such as image recognition, natural language processing, and speech recognition have all seen ra advancements in recent years due to the adaptability of neural networks to contribute to these problems. The ability or neural networks to automatically learn hierarchical representations of data, coupled with their capacity to handle massive amounts of data, make them indispensable in domains where raw input data is abundant and diverse.
Furthermore, neural networks thrive in situations where feature engineering is challenging or impractical. Unlike traditional machine learning algorithms that often rely on handcrafted features, neural networks are adept at automatically extracting relevant features from raw data, alleviating the need for extensive manual feature engineering. This characteristic is particularly advantageous in domains such as computer vision, where the sheer complexity of visual data makes feature engineering a daunting task.
However, despite their impressive capabilities, there are instances where neural networks may not be the most suitable choice. One prominent consideration is the computational cost associated with training and deploying deep neural networks. Training complex neural architectures on large datasets can be computationally intensive and may require specialized hardware such as GPUs or TPUs to achieve reasonable training times. Moreover, deploying neural networks in resource-constrained environments, such as embedded systems or mobile devices, can pose significant challenges due to their computational and memory requirements.
Additionally, neural networks are inherently opaque black-box models, which means they offer limited interpretability compared to some traditional machine learning algorithms. Understanding how a neural network arrives at a particular prediction or decision can be challenging, especially in critical domains where interpretability and transparency are paramount (or where it may be legally required to offer tight justification), such as healthcare or finance. In such scenarios, traditional machine learning algorithms like decision trees or linear models may offer more interpretable and explainable solutions.
Ultimately, the decision to use neural networks over traditional machine learning algorithms depends on several factors, including the nature of the data, the complexity of the task, computational constraints, and the importance of interpretability. While neural networks have revolutionized many fields and are indispensable for tackling complex problems, it’s essential to weigh their strengths against their limitations and carefully evaluate whether they align with the requirements of the specific application at hand.
We can help you make this and other decisions in the development of applications for your business. Reach out to find out more.