Naive Bayes starts with just counting and then moving to probability. Naive Bayes classification is one of the most simple and popular algorithms in data mining or machine learning.
Naive Bayes classification is one of the most simple and popular algorithms in data mining or machine learning (Listed in the top 10 popular algorithms by CRC Press Reference ). The basic idea of the Naive Bayes classification is very simple.
Let’s say, we have books of two categories. One category is Sports and the other is Machine Learning. I count the frequency of the words of “Match” (Attribute 1) and Count of the word “Algorithm” (Attribute 2). Let’s assume, I have a total of 6 books from each of these two categories and the count of words across the six books looks like the below figure.
Figure 1: Count of words across the books
We see that clearly that the word ‘algorithm’ appears more in Machine Learning books and the word ‘match’ appears more in Sports. Powered with this knowledge, Let’s say if I have a book whose category is unknown. I know Attribute 1 has a value 2 and Attribute 2 has a value 10, we can say the book belongs to Sports Category.
Basically we want to find out which category is more likely, given attribute 1 and attribute 2 values.
Deep Learning with scikit-learn: PyTorch, TensorFlow and Caffe aren’t the only frameworks for Deep Learning. There is also a l library with a scikit-learn like API.
Looking to attend an AI event or two this year? Below ... Here are the top 22 machine learning conferences in 2020: ... Start Date: June 10th, 2020 ... Join more than 400 other data-heads in 2020 and propel your career forward. ... They feature 30+ data science sessions crafted to bring specialists in different ...
Naive Bayes Classification. Probability basics and Bayes theorem. Naive Bayes Classification is a supervised machine learning algorithm. It is one of the many algorithms that are derived from the Bayes’ theorem.
Project walk-through on Convolution neural networks using transfer learning. From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects.
Filtering spam with Multinomial Naive Bayes (From Scratch). In the first half of 2020 more than 50% of all email traffic on the planet was spam. Spammers typically receive 1 reply for every 12,500,000 emails sent which doesn’t sound like much until you realize more than 15 billion spam emails are being sent each and every day.