In Naïve Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data. The main advantage is that it can’t learn interactions between features.
In a machine learning interview, when asked about the advantages of Naive Bayes classifier, you can mention several key points:
- Simplicity and Efficiency: Naive Bayes is a simple and easy-to-understand algorithm. It’s computationally efficient, especially for large datasets, as it involves only simple probabilistic calculations.
- Fast Training Speed: Naive Bayes classifiers have fast training speeds compared to more complex algorithms like Support Vector Machines or Neural Networks. This makes it particularly useful for real-time predictions or when computational resources are limited.
- Works Well with High Dimensional Data: Naive Bayes performs well in high-dimensional spaces, such as text classification or document categorization, where the number of features (words) is large compared to the number of samples.
- Robust to Irrelevant Features: Naive Bayes handles irrelevant features gracefully. Since it computes probabilities independently for each feature, irrelevant features don’t impact its performance significantly.
- Strong Performance with Small Datasets: Despite its simplicity, Naive Bayes often performs surprisingly well, especially on small datasets. It doesn’t require a large amount of data to estimate parameters accurately.
- Probabilistic Predictions: Naive Bayes provides not only class labels but also probabilities associated with each prediction. This can be useful in applications where understanding the confidence of predictions is important.
- Ease of Interpretability: The probabilistic nature of Naive Bayes makes it easy to interpret. It provides clear insights into how each feature contributes to the final decision.
Overall, Naive Bayes is a powerful algorithm with several advantages, particularly in scenarios involving text classification, spam filtering, sentiment analysis, and other similar tasks where its assumptions hold true. However, it’s essential to keep in mind its “naive” assumption of feature independence, which might not hold in all real-world scenarios.