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What is Gaussian naive Bayes algorithm?
Gaussian Naive Bayes is a variant of Naive Bayes that follows Gaussian normal distribution and supports continuous data. Naive Bayes are a group of supervised machine learning classification algorithms based on the Bayes theorem. It is a simple classification technique, but has high functionality.
Which type of Naive Bayes classifier is usually used for Yes No?
Bernoulli Naive Bayes: The parameters that we use to predict the class variable take up only values yes or no, for example if a word occurs in the text or not.
What are the pros and cons of Naive Bayes?
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.
What kind of algorithm is naive Bayes classifier?
Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.
Are there any real world uses for naive Bayes?
In spite of their apparently over-simplified assumptions, naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They require a small amount of training data to estimate the necessary parameters.
How is the Bayes algorithm known from training data?
This is known from the training dataset by filtering records where Y=c. The second term is called the prior which is the overall probability of Y=c, where c is a class of Y. In simpler terms, Prior = count(Y=c) / n_Records. An example is better than an hour of theory. So let’s see one.
Which is an example of the naive Bayes theorem?
In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter.