Naive Bayes Algorithm
Advantages of Naive Bayes Algorithm
The Naive Bayes algorithm has several advantages that contribute to its popularity:
- Speed and efficiency: Naive Bayes has a low computational cost, making it fast and efficient even with large datasets.
- Simplicity: Its simplicity makes it easy to implement and understand, even for those new to machine learning.
- Robust against irrelevant features: Naive Bayes can handle irrelevant features and still provide accurate predictions. This makes it suitable for high-dimensional data with many attributes.
Limitations of the Naive Bayes Algorithm
Though Naive Bayes is powerful, it does have some limitations to be aware of:
- Dependence on independence assumption: The assumption of feature independence might not hold in all real-world scenarios. The algorithm’s performance can degrade when features are strongly correlated.
- Sensitivity to input data quality: Naive Bayes is sensitive to the quality of input data. Noisy or erroneous data can impact the algorithm’s accuracy. Preprocessing and feature engineering are essential to address this issue.
