#2020-07-18 12:56:15
Who would have thought such an old basic thought to become so huge, literally change/create statistics field and still be relevant after 200+ years. Naive Bayes is not so versatile but remains a very interesting concept indeed.
#SUPERVISED
#CLASSIFICATION
Naive-Bayes Classifier
- Data Size: Large and Small
- Speed: Fast
- Ease of Use: Easy
- Normalization: No
- Predictor: Categorical
- Primary Problem: Multiclass or Binary
- Mixed-type: No
- Missing Data Handling: Yes
- Popularity: 70%
naive bayes tutorials in python
1- Simple Implementation
Do you want to learn how to implement Naive Bayes Machine Learning Algorithm? You can do so starting now. This is a very simple but fundamental implementation
2- Step by Step
This step-by-step Machine Learning tutorial attempts to provide a little more explanation about the Naive Bayes simple implementation steps that are introduced previously.
3- Optimization
Learn about important parameters when working with random forest algorithms and how to optimize them.
4- Pros & Cons
It's hard to use an algorithm comfortably without knowing different pros and cons that Machine Learning algorithm introduces. In this tutorial we have attempted to objectively name and explain all the advantages and disadvantages that comes with Naive Bayes Algorithms.
5- History
Read about the history of Naive Bayes Algoritm. It's very interesting and even entertaining.