The Naive Bayes classifier is just about counting how many time a word appear in each class.
It is the most simple algorithm that you can apply to your data. As the name suggests, this algorithm makes an assumption as every feature is independent ( not correlated to each other)of the others, in order to predict the class of a given sample
Because it has been proved to not only be simple but also very fast, accurate, reliable and easy to implement: just count words.
The simplest solutions are usually the most powerful ones, and Naive Bayes is…
What is regularization ?
In machine learning, regularization is any modification made to a learning algorithm that is intended to reduce its generalization error.
It is also a technique used for solving the problem of overfitting in a machine learning algorithm by penalizing the cost function. It is just the fact of adding λI to the solution of θ.
What is a regularizer ?
A regularizer is just a hyper-parameter that we add to our model. λ is called the regularization parameter. …