Why naive bayes works well with many number of features?
Because of the class independence assumption, naive Bayes classifiers can quickly learn to use high dimensional features with limited training data compared to more sophisticated methods. This can be useful in situations where the dataset is small compared to the number of features, such as images or texts.
Why naive bayes correlated features affects performance?
Yes, it will affect the performance of Naive Bayes. It is called Naive because it assumes an independence between the features, which in practice is rarely the case. However, it’s shown to be fairly robust to this and to be able to perform well on real-world problems.
Why features are independent in naive bayes?
Naive Bayes is so called because the independence assumptions we have just made are indeed very naive for a model of natural language. The conditional independence assumption states that features are independent of each other given the class. This is hardly ever true for terms in documents.
Which naive bayes model will you use for data set containing continuous feature values?
Gaussian Naïve Bayes Classifier: In Gaussian Naïve Bayes, continuous values associated with each feature are assumed to be distributed according to a Gaussian distribution (Normal distribution).
What is naive Bayes algorithm in machine learning?
Naive Bayes is a machine learning model that is used for large volumes of data, even if you are working with data that has millions of data records the recommended approach is Naive Bayes. It gives very good results when it comes to NLP tasks such as sentimental analysis. It is a fast and uncomplicated classification algorithm.
Which is faster naive Bayes or normal Bayes?
The Naive Bayes classifier is much faster with its probability calculations. This is the kind of algorithm used when all features follow a normal distribution. All features are continuous valued. The assumption is that there is no covariance between the independent features.
When to use naive Bayes classifier in logistic regression?
When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data. It perform well in case of categorical input variables compared to numerical variable (s).
Which is the most simple Bayes algorithm to use?
Naive Bayes is the most simple algorithm that you can apply to your data. As the name suggests, here this algorithm makes an assumption as all the variables in the dataset is “Naive” i.e not correlated to each other.