Contents
Is Naive Bayes robust to noise?
Naive Bayes showed to be more robust against data noise and the two classifiers cross each other around the 50% percent noise. Therefore, when dealing with a data set with over 50% noise, Naive Bayes is recommended.
Why Naive Bayes is robust to irrelevant attributes?
Conclusion. In general, Naive Bayes classifiers are robust to noise data because such noise data are averaged out when estimating the conditional probabilities from all data set. Naive Bayes classifiers are also practical classifiers to handle the irrelevant attributes.
Is decision tree robust to noise?
In most practical problems of classifier learning, the training data suffers from the label noise. Hence, it is important to understand how robust is a learning algorithm to such label noise. Experimentally, Decision trees have been found to be more robust against label noise than SVM and logistic regression.
What kind of problem is naive Bayes used for?
Naive Bayes is a probabilistic algorithm that’s typically used for classification problems. Naive Bayes is simple, intuitive, and yet performs surprisingly well in many cases. For example, spam filters Email app uses are built on Naive Bayes.
Can a spam filter be built on Naive Bayes?
Naive Bayes is simple, intuitive, and yet performs surprisingly well in many cases. For example, spam filters Email app uses are built on Naive Bayes. In this article, I’ll explain the rationales behind Naive Bayes and build a spam filter in Python. (For simplicity, I’ll focus on binary classification problems)
Which is the best choice for continuous Bayes?
For continuous features, there are essentially two choices: discretization and continuous Naive Bayes. Discretization works by breaking the data into categorical values. The simplest discretization is uniform binning, which creates bins with fixed range.
Why is the posterior probability of naive Bayes zero?
This means that Naive Bayes handles high-dimensional data well. For categorical features, the estimation of P (Xi|Y) is easy. However, one issue is that if some feature values never show (maybe lack of data), their likelihood will be zero, which makes the whole posterior probability zero.