naive-bayes-classifier | Implementing Naive Bayes Classification algorithm into PHP

by varunon9 PHP Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | naive-bayes-classifier Summary

naive-bayes-classifier is a PHP library. naive-bayes-classifier has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Implementing Naive Bayes Classification algorithm into PHP to classify given text as ham or spam using MySql database.

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naive-bayes-classifier has a low active ecosystem.

It has 19 star(s) with 4 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of naive-bayes-classifier is current.

Quality

naive-bayes-classifier has 0 bugs and 0 code smells.

Security

naive-bayes-classifier has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

naive-bayes-classifier code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

naive-bayes-classifier is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

naive-bayes-classifier releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

naive-bayes-classifier saves you 43 person hours of effort in developing the same functionality from scratch.

It has 116 lines of code, 5 functions and 4 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed naive-bayes-classifier and discovered the below as its top functions. This is intended to give you an instant insight into naive-bayes-classifier implemented functionality, and help decide if they suit your requirements.

Determine the keywords .
Train a sentence based on given category .
Tokenize a sentence
Class method to classify a sentence .

Get all kandi verified functions for this library.

naive-bayes-classifier Key Features

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naive-bayes-classifier Examples and Code Snippets

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Community Discussions

Trending Discussions on naive-bayes-classifier

Difficulties to get the correct posterior value in a Naive Bayes Implementation

AODE Machine Learning in R

key error not in index while cross validation

QUESTION

Difficulties to get the correct posterior value in a Naive Bayes Implementation

Asked 2020-Nov-12 at 14:44

For studying purposes, I've tried to implement this "lesson" using python but "without" sckitlearn or something similar.

My attempt code is the follow:

...

ANSWER

Answered 2020-Nov-12 at 11:43

You haven't multiplied by the priors p(Sport) = 3/5 and p(Not Sport) = 2/5. So just updating your answers by these ratios will get you to the correct result. Everything else looks good.

So for example you implement p(a|Sports) x p(very|Sports) x p(close|Sports) x p(game|Sports) in your math.prod(p) calculation but this ignores the term p(Sport). So adding this in (and doing the same for the not sport condition) fixes things.

In code this can be achieved by:

Source https://stackoverflow.com/questions/64745233

QUESTION

AODE Machine Learning in R

Asked 2020-Mar-12 at 13:00

I wanted to know if really AODE may be better than Naive Bayes in its way, as the description says:

https://cran.r-project.org/web/packages/AnDE/AnDE.pdf

--> "AODE achieves highly accurate classification by averaging over all of a small space."

https://www.quora.com/What-is-the-difference-between-a-Naive-Bayes-classifier-and-AODE

--> "AODE is a weird way of relaxing naive bayes' independence assumptions. It is no longer a generative model, but it relaxes the independence assumptions in a slightly different (and less principled) way than logistic regression does. It replaces the convex optimization problem used in training a logistic regression classifier by a quadratic (on the number of features) dependency on both training and test times."

But when I experiment it, I found that the predict results seems off, I implemented it with these codes:

...

ANSWER

Answered 2020-Mar-12 at 13:00

If you check out the vignette for the function:

train: data.frame : training data. It should be a data frame. AODE works only discretized data. It would be better to discreetize the data frame before passing it to this function.However, aode discretizes the data if not done before hand. It uses an R package called discretization for the purpose. It uses the well known MDL discretization technique.(It might fail sometimes)

By default, the discretization function from arules cuts it into 3, which may not be enough for iris. So I first reproduce the result you have with the discretization by arules:

Source https://stackoverflow.com/questions/60647274

QUESTION

key error not in index while cross validation

Asked 2019-Apr-24 at 18:12

I have applied svm on my dataset. my dataset is multi-label means each observation has more than one label.

while KFold cross-validation it raises an error not in index.

It shows the index from 601 to 6007 not in index (I have 1...6008 data samples).

This is my code:

...

ANSWER

Answered 2018-Aug-16 at 05:53

train_index, test_index are integer indices based on the number of rows. But pandas indexing dont work like that. Newer versions of pandas are more strict in how you slice or select data from them.

You need to use .iloc to access the data. More information is available here

This is what you need:

Source https://stackoverflow.com/questions/51852551

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install naive-bayes-classifier

Download the project and extract zip. Create database in MySql-. Open a terminal and move to project folder. Edit database connection info in db_connect.php file. Execute main.php php main.php.
Download the project and extract zip.
Create database in MySql- mysql> create database naiveBayes; mysql> use naiveBayes; mysql> create table trainingSet (S_NO integer primary key auto_increment, document text, category varchar(255)); mysql> create table wordFrequency (S_NO integer primary key auto_increment, word varchar(255), count integer, category varchar(255));
Open a terminal and move to project folder
Edit database connection info in db_connect.php file
Execute main.php php main.php

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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