Discover the advantages of classical machine learning compared to deep learning. Get key insights for the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
Which of the following is an advantage of classical machine learning compared to deep learning?
A. Classical machine learning works well with unstructured data.
B. Classical machine learning is more expensive to operate, but gives better results.
C. Classical machine learning outperforms deep learning ecosystems.
D. Classical machine learning is easier to interpret.
Answer
D. Classical machine learning is easier to interpret.
Explanation
Classical machine learning models, such as decision trees and linear regression, are generally simpler and more transparent compared to deep learning models. This simplicity often makes it easier for humans to understand and interpret the decisions that classical machine learning algorithms make.
Model Complexity:
- Classical machine learning models (such as linear regression, decision trees, and support vector machines) tend to be simpler and have fewer parameters.
- Deep learning models (such as neural networks) are highly complex, with many layers and millions of parameters. This complexity makes them harder to interpret.
Feature Importance:
- In classical ML, feature importance can be easily determined. For example, decision trees provide feature importances based on splits.
- In deep learning, understanding which features contribute most to the model’s output is challenging due to the intricate layer interactions.
Black-Box Nature:
- Deep learning models are often considered “black boxes.” They learn complex representations but lack transparency.
- Classical ML models, especially linear models, are more transparent. You can inspect coefficients and understand how input features affect predictions.
Debugging and Troubleshooting:
- Debugging deep learning models can be daunting. Issues may arise from vanishing gradients, overfitting, or incorrect hyperparameters.
- Classical ML models are easier to debug and troubleshoot because their behavior is more predictable.
Human-Readable Rules:
- Some classical ML models (like decision trees) produce human-readable rules. These rules help explain why a prediction was made.
- Deep learning models lack such explicit rules, making it harder to justify their decisions.
However, it’s essential to note that deep learning excels in handling complex data (e.g., images, natural language), where classical ML struggles. Deep learning’s power lies in its ability to learn hierarchical representations from raw data, even if interpretability is sacrificed.
Here’s why the other options are incorrect:
A. Classical machine learning typically works better with structured data, while deep learning excels at handling unstructured data like images, audio, and text.
B. Classical machine learning models are often less expensive to operate than deep learning models, which require more computational resources and specialized hardware like GPUs.
C. Deep learning has outperformed classical machine learning in many domains, particularly in tasks involving complex patterns, such as image and speech recognition.
In summary, while deep learning has surpassed classical machine learning in terms of performance on many tasks, the interpretability of classical machine learning models remains a key advantage. This transparency can be crucial in applications where understanding the decision-making process is essential, such as in healthcare, finance, and other regulated industries.
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