Table of Contents
Why is naive Bayes less likely to Overfit?
It’s more likely to underfit. Naive Bayes is a fairly simple algorithm, making a strong assumption of independence between the features, so it would be biased and less flexible, hence less likely to overfit.
How Overfitting is control in naive Bayes classification algorithm?
Varience (Overfitting): Overfitting in Naive Bayes classifiers are controlled by introducing priors. Minimizing the error function gives other algorithms i.e logistic regression lower bias than naive Bayes because the meaning of “bias” is how much error there is in a model.
Why do naive Bayesian classifiers perform so well?
Advantages. It is easy and fast to predict the class of the test data set. It also performs well in multi-class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.
What are the advantages of naive Bayes?
Advantages of Naive Bayes Classifier It doesn’t require as much training data. It handles both continuous and discrete data. It is highly scalable with the number of predictors and data points. It is fast and can be used to make real-time predictions.
Is naive Bayes immune to Overfitting?
The Naive Bayes classifier employs a very simple (linear) hypothesis function. On the other hand, it exhibits low variance or failure to generalize to unseen data based on its training set, because it’s hypothesis class’ simplicity prevents it from overfitting to its training data.
Why is Naive Bayes so good for text classification?
As the Naive Bayes algorithm has the assumption of the “Naive” features it performs much better than other algorithms like Logistic Regression, Tree based algorithms etc. The Naive Bayes classifier is much faster with its probability calculations.
What are the advantages and disadvantages of naive Bayes classifier?
Naive Bayes is suitable for solving multi-class prediction problems. If its assumption of the independence of features holds true, it can perform better than other models and requires much less training data. Naive Bayes is better suited for categorical input variables than numerical variables.
What are the pros and cons of a naive Bayes classifier?
Pros and Cons of Naive Bayes Algorithm
- The assumption that all features are independent makes naive bayes algorithm very fast compared to complicated algorithms. In some cases, speed is preferred over higher accuracy.
- It works well with high-dimensional data such as text classification, email spam detection.