IBM AI Fundamentals: Advantages and Differences Comparing Classical Machine Learning and Deep Learning

Discover the key advantages of classical machine learning compared to deep learning. Learn about interpretability, performance, cost, and relevance in this comprehensive guide.

Question

Which of the following is an advantage of classical machine learning compared to deep learning?

A. It’s easier to interpret.
B. It’s better performance on big data.
C. It’s more expensive to operate.
D. It’s obsolete.

Answer

A. It’s easier to interpret.

Explanation

Classical machine learning is easier to interpret compared to most methods in the deep learning ecosystem.

Classical machine learning has several advantages compared to deep learning, and one of the most significant is its interpretability. Classical machine learning models, such as decision trees, linear regression, and logistic regression, are more transparent and easier to understand than deep learning models.

Here’s why interpretability is an advantage of classical machine learning:

1. Feature importance: In classical machine learning, it’s easier to identify which features (input variables) have the most significant impact on the model’s predictions. This allows for better understanding of the underlying patterns and relationships in the data.
2. Model transparency: Classical machine learning models are often based on well-established statistical and mathematical principles, making them more transparent and explainable. The decision-making process can be traced back to specific features and their weights, enabling stakeholders to understand how the model arrives at its predictions.
3. Debugging and troubleshooting: When issues arise in classical machine learning models, it’s easier to identify the root cause and make necessary adjustments. The interpretability of these models facilitates debugging and troubleshooting processes.
4. Regulatory compliance: In some industries, such as healthcare and finance, interpretability is crucial for regulatory compliance. Classical machine learning models can provide the required explanations and justifications for their predictions, ensuring compliance with regulations and building trust with stakeholders.

While deep learning models have shown remarkable performance on complex tasks, especially with big data, they are often considered “black boxes” due to their complex architectures and numerous layers. This lack of interpretability can be a drawback in certain scenarios where explainability is essential.

It’s important to note that classical machine learning is not obsolete, as option D suggests. Both classical machine learning and deep learning have their strengths and weaknesses, and the choice between them depends on the specific problem, data characteristics, and project requirements.

In summary, the easier interpretability of classical machine learning models is a significant advantage compared to deep learning, particularly in situations where transparency, explainability, and regulatory compliance are critical factors.

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